- Something like DGX QSFP link (200Gb/s, 400Gb/s) instead of TB5. Otherwise, the economies of this RDMA setup, while impressive, don't make sense.
- Neural accelerators to get prompt prefill time down. I don't expect RTX 6000 Pro speeds, but something like 3090/4090 would be nice.
- 1TB of unified memory in the maxed out version of Mac Studio. I'd rather invest in more RAM than more devices (centralized will always be faster than distributed).
- +1TB/s bandwidth. For the past 3 generations, the speed has been 800GB/s...
- The ability to overclock the system? I know it probably will never happen, but my expectation of Mac Studio is not the same as a laptop, and I'm TOTALLY okay with it consuming +600W energy. Currently it's capped at ~250W.
Also, as the OP noted, this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!! All the more reason for Apple to invest in something like QSFP.
Would you please mind leaving some RAM to remain available for purchase at an affordable price for us mere mortals ? 1Tb for what, like, "Come on AI, make the humankind happy now"?
> +1TB/s bandwidth. For the past 3 generations, the speed has been 800GB/s...
M4 already hit the necessary speed per channel, and M5 is well above it. If they actually release an Ultra that much bandwidth is guaranteed on the full version. Even the smaller version with 25% fewer memory channels will be pretty close.
We already know Max won't get anywhere near 1TB/s since Max is half of an Ultra.
> - The ability to overclock the system? I know it probably will never happen, but my expectation of Mac Studio is not the same as a laptop, and I'm TOTALLY okay with it consuming +600W energy. Currently it's capped at ~250W.
I don't think the Mac Studio has a thermal design capable of dissipating 650W of heat for anything other than bursty workloads. Need to look at the Mac Pro design for that.
The thermal design is irrelevant, and people saying they want insane power density are, in my personal view, deluded ridiculous individuals who understand very very little.
Overclocking long ago was an amazing saintly act, milking a lot of extra performance that was just there waiting, without major downsides to take. But these days, chips are usually already well tuned. You can feed double or tripple the power into the chip with adequate cooling, but the gain is so unremarkable. +10% +15% +20% is almost never going to be a make or break difference for your work, and doing so at double or triple the power budget is an egregious waste.
So many of the chips about are already delivered at way higher than optimum efficiency, largely for bragging rights. The exponential decay of efficiency you keep pushing for is an anti-quest, is against good. The absolute performance wins are ridiculous to seek. In almost all cases.
If your problem will not scale and dumping a ton of power into one GPU or one cpu socket is all you got, fine, your problem is bad and you have to deal with that. But for 90% of people, begging for more power proces you don't actually know jack & my personal recommendation is that all such points of view deserve massive down voting by anyone with half a brain.
Go back to 2018 and look at Matthew Dillon on DragobflyBSD underpowering the heck out of their 2990wx ThreadRipper. Efficiency just soars as you tell the chip to take less power. The situation has not improved! Efficiency skyrockets today at least as much as it did then by telling chips not to go all out. Good chips behave & reward. I believe Apple competent enough to thoroughly disabuse this position that this chip would be far better if we could dump 2x 3x more power into it. Just a fools position, beyond a joke, imo. https://apollo.backplane.com/DFlyMisc/threadripper.txt
I was actually looking for benchmarks earlier this week along those lines - ideally covering the whole slate of Arrow Lake processors running at various TDPs. Not much available on the web though.
I learned a lot about underclocking, undervolting, and computational power efficiency during my brief time in the ethereum mining[1] shenanigans. The best ROI was with the most-numerous stable computations at the lowest energy expense.
I'd tweak individual GPUs' various clocks and volts to optimize this. I'd even go so far as to tweak fan speed ramps on the cards themselves (those fans don't power themselves! There's whole Watts to save there!).
I worked to optimize the efficiency of even the power from the wall.
But that was a system that ran, balls-out, 24/7/365.
Or at least it ran that way until it got warmer outside, and warmer inside, and I started to think about ways to scale mining eth in the basement vs. cooling the living space of the house to optimize returns. (And I never quite got that sorted before they pulled the rug on mining.)
And that story is about power efficiency, but: Power efficiency isn't always the most-sensible goal. Sometimes, maximum performance is a better goal. We aren't always mining Ethereum.
Jeff's (quite lovely) video and associated article is a story about just one man using a stack of consumer-oriented-ish hardware in amusing -- to him -- ways, with local LLM bots.
That stack of gear is a personal computer. (A mighty-expensive one on any inflation-adjusted timeline, but what was constructed was definitely used as a personal computer.)
Like most of our personal computers (almost certainly including the one you're reading this on), it doesn't need to be optimized for a 24/7 100% workload. It spends a huge portion of its time waiting for the next human input. And unlike mining Eth in the winter in Ohio: Its compute cycles are bursty, not constant, and are ultimately limited by the input of one human.
So sure: I, like Jeff, would also like to see how it would work when running with the balls[2] running further out. For as long as he gets to keep it, the whole rig is going to spend most of its time either idling or off, anyway. So it might as well get some work done when a human is in front of it, even if each token costs more in that configuration than it does OOTB.
It theoretically can even clock up when being actively-used (and suck all the power), and clock back down when idle (and resume being all sleepy and stuff).
That's a well-established concept that [eg] Intel has variously called SpeedStep and/or Turbo Boost -- and those things work for bursty workloads, and have worked in that way for a very long time now.
[1]: Y'all can hate me for being a small part of that problem. It's allowed.
>> people saying they want insane power density are, in my personal view, deluded ridiculous individuals who understand very very little.
Or they are simply not-rich people who cannot afford to purchase extra hardware to run in parallel. Electricity is cheap. GPUs are not. So i want to get every ounce of power out of the precious few GPUs i can afford to own.
(And dont point at clouds. Running AI on someone else's cloud is like telling a shadetree mechanic to rent a car instead of fixing his owm.)
> Also, as the OP noted, this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac
I do wonder where this limitation comes from, since on the M3 Ultra Mac Studios the front USB-C ports are also Thunderbolt 5, for a total of six Thunderbolt ports: https://www.apple.com/mac-studio/specs/
From my brief discussion with Exo/Apple, it sounds like that is just a limitation of this initial rollout, but it's not a hardware limitation.
Though, I am always leery to recommend any decisions be made over something that's not already proven to work, so I would say don't bet on all ports being able to be used. They very well may be able to though.
> Do you really need a fully connected mesh? Doesn't Thunderbolt just show up as a network connection that RDMA is ran on top of?
If you daisy chain four nodes, then traffic between nodes #1 and #4 eat up all of nodes #2 and #3's bandwidth, and you eat a big latency penalty. So, absent a switch, the fully connected mesh is the only way to have fast access to all the memory.
Might be helpful if they actually provided a programming model for ANE that isn't onnx. ANE not having a native development model just means software support will not be great.
For a company that has repeatedly ignored macOS, your wishlist seems anything but a pipe dream. QSFP on a mac. Yeah right. If anything, they’ll double down on TB or some nonstandard interconnect.
What is a computer?
(Although, I do hope with the new work on supporting RDMA, the MLX5 driver shipped with macOS will finally support RDMA for ConnectX NICs)
it's just people looking to do experiments locally on the main machine rather than just get a dedicated spark, which can be used properly as a headless box than a Mac of which you are at the mercy of system shenanigans albiet still bearable compared to windows
Apple has always sucked at properly embracing properly robust tech for high-end gear for markets outside of individual prosumer or creatives. When Xserves existed, they used commodity IDE drives without HA or replaceable PSUs that couldn't compete with contemporary enterprise servers (HP-Compaq/Dell/IBM/Fujitsu). Xserve RAID interconnection half-heartedly used fiber channel but couldn't touch a NetApp or EMC SAN/filer. I'm disappointed Apple has a persistent blindspot preventing them from succeeding in data center-quality gear category when they could've had virtualized servers, networking, and storage, things that would eventually find their way into my home lab after 5-7 years.
Enterprise never ever mattered, and there arent enough digits available to show your “home lab” use case in the revenue numbers. Xserve, the RAID shelves, and the directory services were kinda there as a half hearted attempt for that late 90-00s AV setup. All of that fell on the cutting room floor once personal devices, esp iphone, was realized.
By the time I left in ‘10 the total revenue from mac hardware was like 15% of revenue. Im honestly surprised theres anyone who cared enough to package the business services for mac minis.
So if everything else is printing cash for a HUGE addressable consumer market at premium price points why would they try and compete with their own ODMs on more-or-less commodity enterprise gear?
Seems like I remember the main reason Macs survived as a product at all was because you needed one to develop for iOS. That may be an exaggeration but there certainly was a time when Macs were few and far between outside of creative shops. Certainly they were almost unseen in the corporate world, where now they are fairly common at least in laptops.
Macs survived because Apple got a cash injection, survived long enough to come out with colorful iMacs with an hockey puck mouse, still running on Mac OS 8, and the iPod.
Requiring one for doing iOS development they were already back into the green.
Considering that Apple is moving away from Linux in the datacenter to its own devices, I'm not sure that's the case. The apple machines aren't available to the consumer (they're rack-mounted, dozens of chips per PCB board, custom-made machines) but they're much less power-hungry, just as fast (or more so), much cheaper for them to make rather than buy, and natively support their own ecosystem.
Some of the machine-designs that consumers are able to buy seem to have a marked resemblance to the feature-set that the datacenter people were clamouring for. Just saying...
> I'm disappointed Apple has a persistent blindspot preventing them from succeeding in ... things that would eventually find their way into my home lab after 5-7 years.
I can see the dollar signs in their eyes right now.
Aftermarkets are a nice reflection of durable value, and there's a massive one for iPhones and a smaller one for quick flameout startup servers, but not much money in 5 - 7 year old servers.
> Also, as the OP noted, this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!! All the more reason for Apple to invest in something like QSFP.
This isn’t any different with QSFP unless you’re suggesting that one adds a 200GbE switch to the mix, which:
* Adds thousands of dollars of cost,
* Adds 150W or more of power usage and the accompanying loud fan noise that comes with that,
* And perhaps most importantly adds measurable latency to a networking stack that is already higher latency than the RDMA approach used by the TB5 setup in the OP.
Wow, this switch (MikroTik CRS812) is scary good for the price point. A quick Google search fails to find any online vendors with stock. I guess it is very popular! Retail price will be <= 1300 USD.
I did some digging to find the switching chip: Marvell 98DX7335
Again, those are some wild numbers if I have the correct model. Normally, Mikrotik includes switching bandwidth in their own specs, but not in this case.
They are very popular and make quite good products, but as you noticed it can be tricky to find them in stock.
Besides stuff like this switch they've also produced pretty cool little micro-switches you can PoE and run as WLAN hotspots, e.g. to distance your mobile user device from some network you don't really trust, or more or less maliciously bridge a cable network through a wall because your access to the building is limited.
That switch appears to have 2x 400G ports, 2x 200G ports, 8x 50G ports, and a pair of 10G ports. So unless it allows bonding together the 50G ports (which the switch silicon probably supports at some level), it's not going to get you more than four machines connected at 200+ Gbps.
As with most 40+GbE ports, the 400Gbit ports can be split into 2x200Gbit ports with the use of special cables. So you can connect a total of 6 machines at 200Gbit.
Ah, good point. Though if splitter cables are an option, then it seems more likely that the 50G ports could be combined into a 200G cable. Marvell's product brief for that switch chip does say it's capable of operating as an 8x 200G or 4x 400G switch, but Mikrotik may need to do something on their end to enable that configuration.
You’re talking about link aggregation (LACP) here, which requires specific settings on both the switch and client machine to enable, so while it’s likely possible to combine 50Gbps ports like you describe, that’s not what I was referring to.
I'm not trolling here: Do you think that Marvell sells the chips wholesale buy the vendor buys the feature set (IP/drivers/whatever)? That would allow Marvell to effectively sell the same silicon but segment the market depending upon what buyers needs. Example: A buyer might need a config that is just a bunch of 50GB/s ports and another 100GB/s ports and another a mix. (I'm thinking about blowing fuses in the manuf phase, similar to what AMD and Intel do.) I write this as a complete noob in switching hardware.
Cool! So for marginally less in cost and power usage than the numbers I quoted, you can get 2 more machines than with the RDMA setup. And you’ve still not solved the thing that I called out as the most important drawback.
The OP makes reference to this with a link to a GitHub repo that has some benchmarks. TCP over Thunderbolt compared to RDMA over Thunderbolt has roughly 7-10x higher latency, ~300us vs 30-50us. I would expect TCP over 200GbE to have similar latency to TCP over Thunderbolt.
Put another way, see the graphs in the OP where he points out that the old way of clustering performs worse the more machines you add? I’d expect that to happen with 200GbE also.
And with a switch, it would likely be even worse, since the hop to the switch adds additional latency that isn’t a factor in the TB5 setup.
RDMA for new AI/HPC clusters is moving toward ethernet (the keyword to look for is RoCE). Ethernet gear is so much cheaper that you can massively over-provision to make up for some of the disadvantages of asynchronous networking, and it lets your run jobs on hyperscalers (only Azure ever supported actual IB). Most HPC is not latency-sensitive enough that it needs Infiniband’s lower jitter/median, and vendors have mostly caught up on the hardware acceleration front.
Hey Jeff, wherever you are: this is awesome work! I’ve wanted to try something like this for a while and was very excited for the RDMA over thunderbolt news.
But I mostly want to say thanks for everything you do. Your good vibes are deeply appreciated and you are an inspiration.
I wonder what motivates apple to release features like RDMA which are purely useful for server clusters, while ignoring basic qol stuff like remote management or rack mount hardware. It’s difficult to see it as a cohesive strategy.
Makes one wonder what apple uses for their own servers. I guess maybe they have some internal M-series server product they just haven’t bothered to release to the public, and features like this are downstream of that?
> I guess maybe they have some internal M-series server product they just haven’t bothered to release to the public, and features like this are downstream of that?
Or do they have some real server-grade product coming down the line, and are releasing this ahead of it so that 3rd party software supports it on launch day?
I worked on some of the internal server hardware. Yes they do have their own boards. Apple used to be all-in on Linux, but the newer chips are far and away more power-efficient, and power is one of the (if not the) major cost of outfitting a datacenter, at least over time.
These machines are very much internal - you can cram a lot of M-series (to use the public nomenclature) chips onto a rack-sized PCB. I was never under the impression they were destined for anything other than Apple datacenters though...
As I mentioned above, it seems to me there's a couple of feature that appeared on the customer-facing designs that were inspired by what the datacenter people wanted on their own PCB boards.
That they sell to the public? No way. They’ve clearly given up on server stuff and it makes sense for them.
That they use INTERNALLY for their servers? I could certainly see this being useful for that.
Mostly I think this is just to get money from the AI boom. They already had TB5, it’s not like this was costing them additional hardware. Just some time that probably paid off on their internal model training anyway.
The Mac Studio, in some ways, is in a class of its own for LLM inference. I think this is Apple leaning into that. They didn't add RDMA for general server clustering usefulness. They added it so you can put 4 Studios together in an LLM inferencing cluster exactly as demonstrated in the article.
I honestly forgot they still made the Mac Pro. Amazing that they have these ready to ship on their website. But at a 50% premium over similar but faster Mac Studio models, what is the point? You can't usefully put GPUs in them as far as I know. You'd have to have a different PCIe need to make it make sense.
AWS is just used for storage, because it's cheaper than Apple maintaining it, itself. Apple do have storage-datacenter at their campus at least (I've walked around one, it's many many racks of SSD's) but almost all the public stuff is on AWS (wrapped up in encryption) AFAIK.
Apple datacenters are mainly compute, other than the storage you need to run the compute efficiently.
I assume a company like Apple either has custom server boards with tons of unified memory on M series with all the i/o they could want (that are ugly and thus not productized) or just use standard expensive nvidia stuff like everyone else.
It’s quite interesting how „boring“ (traditionally enterprise?) their backend looks on the occasional peeks you get publicly. So much Apache stuff & XML.
The annoying thing is there's no ability to control power (or see system metrics) outside the chassis. With servers and desktop PCs, you can usually tap into power pins and such.
Linux already has RDMA support but it cannot yet use Thunderbolt. It's probably quite a bit of work to add everything that's required. Is anyone working on it?
It would be great to have this for those cheap Strix Halo boxes with 128GB quad channel DDR5-8000 for using two or three of them with their 2 USB4 ports (which are Thunderbolt capable) to fit larger models.
I'd be interested in seeing numbers that split out the speed of reading input (aka prefill) and the speed of generating output (aka decode). Those numbers are usually different and I remember from this Exo article that they could be quite radically different on Mac hardware: https://blog.exolabs.net/nvidia-dgx-spark/
See https://github.com/geerlingguy/beowulf-ai-cluster/issues/17 for more data — I didn't save all the prompt processing times (Exo just outputs a time in ms, no other data for that), but will try to have another pass. Maybe also convince the Exo team to add a proper benchmarking capability ala `llama-bench` :)
They are now, this morning they pushed all the code to the Exo repo, and archived the earlier Exo branch. We'll see how open they are now that whatever embargoed work they did with Apple is public..
10G Ethernet would only marginally speed things up based on past experience with llama RPC; latency is much more helpful but still, diminishing returns with that layer split.
Possibly RDMA over thunderbolt. But for RoCE (RDMA over converged Ethernet) obviously not because it's sitting on top of Ethernet. Now that could still have a higher throughput when you factor in CPU time to run custom protocols that smart NICs could just DMA instead, but the overhead is still definitively higher
The "all nodes connecting to all other nodes" setup reminds me of NUMALink, the interconnect that SGI used on many (most? all?) of their supercomputers. In an ideal configuration, each 4-socket node has two NUMALink connections to every other node. As Jeff says, it's a ton of cables, and you don't have to think of framing or congestion in the same way as with RDMA over Ethernet.
SGI's HW also had ccNUMA (cache-coherent Non-Uniform Memory Access), which, given the latencies possible in systems _physically_ spanning entire rooms, was quite a feat.
The IRIX OS even had functionality to migrate kobs and theor working memory closer to each other to lower the latency of access.
We see echoes of this when companies like high-frequency traders pay attention to motherboard layouts and co-locate and pin the PTS (proprietary trading systems) processes to specific cores based on which DIMMs are on which side of the memory controller.
I would have expected that going from one node (which can't hold the weights in RAM) to two nodes would have increased inference speed by more than the measured 32% (21.1t/s -> 27.8t/s).
With no constraint on RAM (4 nodes) the inference speed is less than 50% faster than with only 512GB.
Weights are read-only data so they can just be memory mapped and reside on SSD (only a small fraction will be needed in VRAM at any given time), the real constraint is activations. MoE architecture should help quite a bit here.
You need all the weights every token, so even with optimal splitting the fraction of the weights you can farm out to an SSD is proportional to how fast your SSD is compared to your RAM.
You'd need to be in a weirdly compute-limited situation before you can replace significant amounts of RAM with SSD, unless I'm missing something big.
> MoE architecture should help quite a bit here.
In that you're actually using a smaller model and swapping between them less frequently, sure.
Even with MoE you still need enough memory to load all experts. For each token, only 8 experts (out of 256) are activated, but which experts are chosen changes dynamically based on the input. This means you'll be constantly loading and unloading experts from disk.
MoEs is great for distributed deployments, because you can maintain a distribution of experts that matches your workload, and you can try to saturate each expert and thereby saturate each node.
With a cluster of two 512GB nodes, you have to send half the weights (350GB) over a TB5 connection. But you have to do this exactly once on startup.
With a single 512GB node, you'll be loading weights from disk each time you need a different expert, potentially for each token. Depending on how many experts you're loading, you might be loading 2GB to 20GB from disk each time.
Unless you're going to shut down your computer after generating a couple of hundred tokens, the cluster wins.
> only a small fraction will be needed in VRAM at any given time
I don't think that's true. At least not without heavy performance loss in which case "just be memory mapped" is doing a lot of work here.
By that logic GPUs could run models much larger than their VRAM would otherwise allow, which doesn't seem to be the case unless heavy quantization is involved.
Existing GPU API's are sadly not conducive to this kind of memory mapping with automated swap-in. The closest thing you get AIUI is "sparse" allocations in VRAM, such that only a small fraction of your "virtual address space" equivalent is mapped to real data, and the mapping can be dynamic.
Very cool, I’m probably thinking too much but why are they seemingly hyping this now (I’ve seen a bunch of this recently) with no M5 Max/Ultra machines in sight. Is it because their release is imminent (I have heard Q1 2026) or is it to try and stretch out demand for M4 Max / M3 Ultra. I plan to buy one (not four) but would feel like I’m buying something that’s going to be immediately out of date if I don’t wait for the M5.
I imagine that they want to give developers time to get their RDMA support stabilized, so third party software will be ready to take advantage of RDMA when the M5 Ultra lands.
I definitely would not be buying an M3 Ultra right now on my own dime.
Does it actually creates a unified memory pool? it looks more like an accelerated backend for a collective communications library like nccl, which is very much not unified memory.
The yearly release cadence annoys me to no end. There is literally zero reason to have a new CPU generation every year, it just devalues Mac hardware faster.
Which I guess is the point of this for Apple, but still.
Any thoughts on the GB300 workstation with 768GB RAM (from NVIDA, Asus, Dell, ...)?
Although many announcements were made it seems not to be available yet.
It does have faster interconnects but will probably be much more expensive.
I wonder if there's any possibility that an RDMA expansion device could exist in the future - i.e. a box full of RAM on the other end of a thunderbolt cable. Although I guess such a device would cost almost as much as a mac mini in any case...
RDMA is not really intended for this. RDMA is really just a bunch of functionality of a PCIe device, and even PCIe isn’t really quite right to use like RAM because its cache semantics aren’t intended for this use case.
But the industry knows this, and there’s a technology that is electrically compatible with PCIe that is intended for use as RAM among other things: CXL. I wonder if a anyone will ever build CXL over USB-C.
You still need an interface which does at least two things: handles incoming read/write requests using some kind of network protocol, and operates as a memory controller for the RAM.
Texas Memory Systems was in the business of making large 'RAM Drives'. They had a product line known as "RamSan" which made many gigabytes/terabytes of DDR available via a block storage interface over infiniband and fibre channel. The control layer was implemented via FPGA.
I recall a press release from 2004 which publicized the US govt purchase of a 2.5TB RamSan. They later expanded into SSDs and were acquired by IBM in 2012.
> Working with some of these huge models, I can see how AI has some use, especially if it's under my own local control. But it'll be a long time before I put much trust in what I get out of it—I treat it like I do Wikipedia. Maybe good for a jumping-off point, but don't ever let AI replace your ability to think critically!
It is a little sad that they gave someone an uber machine and this was the best he could come up with.
Question answering is interesting but not the most interesting thing one can do, especially with a home rig.
The realm of the possible
Video generation: CogVideoX at full resolution, longer clips
Mochi or Hunyuan Video with extended duration
Image generation at scale:
FLUX batch generation — 50 images simultaneously
Fine-tuning:
Actually train something — show LoRA on a 400B model, or full fine-tuning on a 70B
but I suppose "You have it for the weekend" means chatbot go brrrrr and snark
Yea, I don't understand why people use LLMs for "facts". You can get them from Wikipedia or a book.
Use them for something creative, write a short story on spec, generate images.
Or the best option: give it tools and let it actually DO something like "read my message history with my wife, find top 5 gift ideas she might have hinted at and search for options to purchase them" - perfect for a local model, there's no way in hell I'd feed my messages to a public LLM, but the one sitting next to me that I can turn off the second it twitches the wrong way? - sure.
> Yea, I don't understand why people use LLMs for "facts". You can get them from Wikipedia or a book.
Because web search is so broken these days, if you want a clean answer instead of wading through pages of SEO nonsense. It's really common (even) amongst non-techy friends that "I'll ask ChatGPT" has replaced "I'll Google it".
I really hope AMD or Intel can get on the clue train and respond.
Intel in particular has half a decade of having extremely amazing Thunderbolt ports on their mobile chips, built in (alas not present on desktop chips, for shame). There's been not bad but not great thunderbolt host-to-host networking, that TCP can go over, but the system to system connectivity had been a total afterthought, not at all tuned for obvious smart readily available options like RDMA here. But nothing stops anyone from having better host-to-host protocols.
There are also so many smart good excellent next steps competitors could go for. CXL is showing up on server systems as a much lighter weight much lower latency transport that is PCIe PHY compatible but lighter weight. Adding this to consumer chips and giving even a third of a shit could blow what we see here out of the water. It could probably be done over USB4 & radically blast this bespoke RDMA capability.
Connectivity had been a bespoke special capability for too long. Intel did amazing with Xeon having integrated OmniPath 100Gb a long time ago, that was amazing, for barely any extra bucks. But the market didn't reward them kicking total ass and everyone gave up on connecting chips together. Today we are hostage to fantastically expensive shitty inefficient NIC that cost a crap ton of money to do a worse job, paying enormous penalty for not having the capability on chip, making at best asmedia io hubs do the USB4 dance a hip away from the CPU.
I really hope Intel can appreciate how good they were, see the threat of Apple kicking as here doing what Intel uniquely has been offering for half a decade with incredible Thunderbolt offerings on-chip (limited alas only to mobile chips). I hope AMD feels the heat and gets some god dMned religion and sees the pressure and thread: man they delivered so strong on PCIe lane counts but man they have been so so so slacking on io capabilities for so long, especially on consumer platforms, and Apple is using both their awesome awesome awesome on-chip memory here and their fan-tastic exceptional ability to care just even the tiniest bit about using the consumer interconnect (that already exists in hardware).
I really really really hope someone else other than Apple can ante up and care. There are so many wins to be had, so close. These companies feel so distracted from the plot. Fucking shame. Good on Apple for being the only mofos to a Tually seize the obvious that was just sitting here, they took no effort nor innovation. What a shame no other players are trying at all.
Intel is allergic for making consumer stuff good. Remember how in consumer range like half of the chips had fucking virtualisation disabled, long after competition had it on everything ?
My expectations from M5 Max/Ultra devices:
- Something like DGX QSFP link (200Gb/s, 400Gb/s) instead of TB5. Otherwise, the economies of this RDMA setup, while impressive, don't make sense.
- Neural accelerators to get prompt prefill time down. I don't expect RTX 6000 Pro speeds, but something like 3090/4090 would be nice.
- 1TB of unified memory in the maxed out version of Mac Studio. I'd rather invest in more RAM than more devices (centralized will always be faster than distributed).
- +1TB/s bandwidth. For the past 3 generations, the speed has been 800GB/s...
- The ability to overclock the system? I know it probably will never happen, but my expectation of Mac Studio is not the same as a laptop, and I'm TOTALLY okay with it consuming +600W energy. Currently it's capped at ~250W.
Also, as the OP noted, this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!! All the more reason for Apple to invest in something like QSFP.
Would you please mind leaving some RAM to remain available for purchase at an affordable price for us mere mortals ? 1Tb for what, like, "Come on AI, make the humankind happy now"?
/"s"
> +1TB/s bandwidth. For the past 3 generations, the speed has been 800GB/s...
M4 already hit the necessary speed per channel, and M5 is well above it. If they actually release an Ultra that much bandwidth is guaranteed on the full version. Even the smaller version with 25% fewer memory channels will be pretty close.
We already know Max won't get anywhere near 1TB/s since Max is half of an Ultra.
> - The ability to overclock the system? I know it probably will never happen, but my expectation of Mac Studio is not the same as a laptop, and I'm TOTALLY okay with it consuming +600W energy. Currently it's capped at ~250W.
I don't think the Mac Studio has a thermal design capable of dissipating 650W of heat for anything other than bursty workloads. Need to look at the Mac Pro design for that.
The thermal design is irrelevant, and people saying they want insane power density are, in my personal view, deluded ridiculous individuals who understand very very little.
Overclocking long ago was an amazing saintly act, milking a lot of extra performance that was just there waiting, without major downsides to take. But these days, chips are usually already well tuned. You can feed double or tripple the power into the chip with adequate cooling, but the gain is so unremarkable. +10% +15% +20% is almost never going to be a make or break difference for your work, and doing so at double or triple the power budget is an egregious waste.
So many of the chips about are already delivered at way higher than optimum efficiency, largely for bragging rights. The exponential decay of efficiency you keep pushing for is an anti-quest, is against good. The absolute performance wins are ridiculous to seek. In almost all cases.
If your problem will not scale and dumping a ton of power into one GPU or one cpu socket is all you got, fine, your problem is bad and you have to deal with that. But for 90% of people, begging for more power proces you don't actually know jack & my personal recommendation is that all such points of view deserve massive down voting by anyone with half a brain.
Go back to 2018 and look at Matthew Dillon on DragobflyBSD underpowering the heck out of their 2990wx ThreadRipper. Efficiency just soars as you tell the chip to take less power. The situation has not improved! Efficiency skyrockets today at least as much as it did then by telling chips not to go all out. Good chips behave & reward. I believe Apple competent enough to thoroughly disabuse this position that this chip would be far better if we could dump 2x 3x more power into it. Just a fools position, beyond a joke, imo. https://apollo.backplane.com/DFlyMisc/threadripper.txt
Oh, we're largely on the same page there.
I was actually looking for benchmarks earlier this week along those lines - ideally covering the whole slate of Arrow Lake processors running at various TDPs. Not much available on the web though.
I learned a lot about underclocking, undervolting, and computational power efficiency during my brief time in the ethereum mining[1] shenanigans. The best ROI was with the most-numerous stable computations at the lowest energy expense.
I'd tweak individual GPUs' various clocks and volts to optimize this. I'd even go so far as to tweak fan speed ramps on the cards themselves (those fans don't power themselves! There's whole Watts to save there!).
I worked to optimize the efficiency of even the power from the wall.
But that was a system that ran, balls-out, 24/7/365.
Or at least it ran that way until it got warmer outside, and warmer inside, and I started to think about ways to scale mining eth in the basement vs. cooling the living space of the house to optimize returns. (And I never quite got that sorted before they pulled the rug on mining.)
And that story is about power efficiency, but: Power efficiency isn't always the most-sensible goal. Sometimes, maximum performance is a better goal. We aren't always mining Ethereum.
Jeff's (quite lovely) video and associated article is a story about just one man using a stack of consumer-oriented-ish hardware in amusing -- to him -- ways, with local LLM bots.
That stack of gear is a personal computer. (A mighty-expensive one on any inflation-adjusted timeline, but what was constructed was definitely used as a personal computer.)
Like most of our personal computers (almost certainly including the one you're reading this on), it doesn't need to be optimized for a 24/7 100% workload. It spends a huge portion of its time waiting for the next human input. And unlike mining Eth in the winter in Ohio: Its compute cycles are bursty, not constant, and are ultimately limited by the input of one human.
So sure: I, like Jeff, would also like to see how it would work when running with the balls[2] running further out. For as long as he gets to keep it, the whole rig is going to spend most of its time either idling or off, anyway. So it might as well get some work done when a human is in front of it, even if each token costs more in that configuration than it does OOTB.
It theoretically can even clock up when being actively-used (and suck all the power), and clock back down when idle (and resume being all sleepy and stuff).
That's a well-established concept that [eg] Intel has variously called SpeedStep and/or Turbo Boost -- and those things work for bursty workloads, and have worked in that way for a very long time now.
[1]: Y'all can hate me for being a small part of that problem. It's allowed.
[2]: https://en.wikipedia.org/wiki/Centrifugal_governor
>> people saying they want insane power density are, in my personal view, deluded ridiculous individuals who understand very very little.
Or they are simply not-rich people who cannot afford to purchase extra hardware to run in parallel. Electricity is cheap. GPUs are not. So i want to get every ounce of power out of the precious few GPUs i can afford to own.
(And dont point at clouds. Running AI on someone else's cloud is like telling a shadetree mechanic to rent a car instead of fixing his owm.)
> Also, as the OP noted, this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac
I do wonder where this limitation comes from, since on the M3 Ultra Mac Studios the front USB-C ports are also Thunderbolt 5, for a total of six Thunderbolt ports: https://www.apple.com/mac-studio/specs/
He corrected that in the comment section of the youtube video. Six is actually the maximum amount. He just didn't want to buy another one.
He also published the Benchmarks in Detail and with two/four Macs in Comparison: https://github.com/geerlingguy/beowulf-ai-cluster/issues/17
Jeff mentioned in the video that only three of the ports can be used for RDMA. But it’s unclear where that limitation is coming from.
From my brief discussion with Exo/Apple, it sounds like that is just a limitation of this initial rollout, but it's not a hardware limitation.
Though, I am always leery to recommend any decisions be made over something that's not already proven to work, so I would say don't bet on all ports being able to be used. They very well may be able to though.
I bet there is one piece of silicon per two ports.
> Neural accelerators to get prompt prefill time down.
Apple Neural Engine is a thing already, with support for multiply-accumulate on INT8 and FP16. AI inference frameworks need to add support for it.
> this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!!
Do you really need a fully connected mesh? Doesn't Thunderbolt just show up as a network connection that RDMA is ran on top of?
> Do you really need a fully connected mesh? Doesn't Thunderbolt just show up as a network connection that RDMA is ran on top of?
If you daisy chain four nodes, then traffic between nodes #1 and #4 eat up all of nodes #2 and #3's bandwidth, and you eat a big latency penalty. So, absent a switch, the fully connected mesh is the only way to have fast access to all the memory.
Might be helpful if they actually provided a programming model for ANE that isn't onnx. ANE not having a native development model just means software support will not be great.
onnx supports CoreML, is that how?
They were talking about neural accelerators (a silicon piece on GPU): https://releases.drawthings.ai/p/metal-flashattention-v25-w-...
> Apple Neural Engine is a thing already, with support for multiply-accumulate on INT8 and FP16. AI inference frameworks need to add support for it.
Or, Apple could pay for the engineers to add it.
Apple already paid software engineers to add Tensorflow support for the ANE hardware.
How much of an improvement can be expected here? It seems to me that in general most potential is pretty quickly realized on Apple platforms.
For a company that has repeatedly ignored macOS, your wishlist seems anything but a pipe dream. QSFP on a mac. Yeah right. If anything, they’ll double down on TB or some nonstandard interconnect.
What is a computer?
(Although, I do hope with the new work on supporting RDMA, the MLX5 driver shipped with macOS will finally support RDMA for ConnectX NICs)
https://kittenlabs.de/blog/2024/05/17/25gbit/s-on-macos-ios/
> 3090 would be nice
They would need 3x speedup over the current generation to approach 3090. A100 that has +- the 3090 compute but 80GB VRAM (so fits LLaMA 70B) does prefill at 550tok/s on a single GPU: https://www.reddit.com/r/LocalLLaMA/comments/1ivc6vv/llamacp...
the GB10 is only the same performance as a 3090. gb10 uses way less power.
i'm not sure why anyone would buy a mac studio instead of a gb10 machine for this use case.
it's just people looking to do experiments locally on the main machine rather than just get a dedicated spark, which can be used properly as a headless box than a Mac of which you are at the mercy of system shenanigans albiet still bearable compared to windows
Apple has always sucked at properly embracing properly robust tech for high-end gear for markets outside of individual prosumer or creatives. When Xserves existed, they used commodity IDE drives without HA or replaceable PSUs that couldn't compete with contemporary enterprise servers (HP-Compaq/Dell/IBM/Fujitsu). Xserve RAID interconnection half-heartedly used fiber channel but couldn't touch a NetApp or EMC SAN/filer. I'm disappointed Apple has a persistent blindspot preventing them from succeeding in data center-quality gear category when they could've had virtualized servers, networking, and storage, things that would eventually find their way into my home lab after 5-7 years.
Enterprise never ever mattered, and there arent enough digits available to show your “home lab” use case in the revenue numbers. Xserve, the RAID shelves, and the directory services were kinda there as a half hearted attempt for that late 90-00s AV setup. All of that fell on the cutting room floor once personal devices, esp iphone, was realized.
By the time I left in ‘10 the total revenue from mac hardware was like 15% of revenue. Im honestly surprised theres anyone who cared enough to package the business services for mac minis.
So if everything else is printing cash for a HUGE addressable consumer market at premium price points why would they try and compete with their own ODMs on more-or-less commodity enterprise gear?
Seems like I remember the main reason Macs survived as a product at all was because you needed one to develop for iOS. That may be an exaggeration but there certainly was a time when Macs were few and far between outside of creative shops. Certainly they were almost unseen in the corporate world, where now they are fairly common at least in laptops.
Macs survived because Apple got a cash injection, survived long enough to come out with colorful iMacs with an hockey puck mouse, still running on Mac OS 8, and the iPod.
Requiring one for doing iOS development they were already back into the green.
For Apple, datacenter stuff is low margin business
Considering that Apple is moving away from Linux in the datacenter to its own devices, I'm not sure that's the case. The apple machines aren't available to the consumer (they're rack-mounted, dozens of chips per PCB board, custom-made machines) but they're much less power-hungry, just as fast (or more so), much cheaper for them to make rather than buy, and natively support their own ecosystem.
Some of the machine-designs that consumers are able to buy seem to have a marked resemblance to the feature-set that the datacenter people were clamouring for. Just saying...
> I'm disappointed Apple has a persistent blindspot preventing them from succeeding in ... things that would eventually find their way into my home lab after 5-7 years.
I can see the dollar signs in their eyes right now.
Aftermarkets are a nice reflection of durable value, and there's a massive one for iPhones and a smaller one for quick flameout startup servers, but not much money in 5 - 7 year old servers.
> Also, as the OP noted, this setup can support up to 4 Mac devices because each Mac must be connected to every other Mac!! All the more reason for Apple to invest in something like QSFP.
This isn’t any different with QSFP unless you’re suggesting that one adds a 200GbE switch to the mix, which:
* Adds thousands of dollars of cost,
* Adds 150W or more of power usage and the accompanying loud fan noise that comes with that,
* And perhaps most importantly adds measurable latency to a networking stack that is already higher latency than the RDMA approach used by the TB5 setup in the OP.
Mikrotik has a switch that can do 6x200g for ~$1300 and <150W.
https://www.bhphotovideo.com/c/product/1926851-REG/mikrotik_...
Wow, this switch (MikroTik CRS812) is scary good for the price point. A quick Google search fails to find any online vendors with stock. I guess it is very popular! Retail price will be <= 1300 USD.
I did some digging to find the switching chip: Marvell 98DX7335
Seems confirmed here: https://cdn.mikrotik.com/web-assets/product_files/CRS812-8DS...
And here: https://cdn.mikrotik.com/web-assets/product_files/CRS812-8DS...
From Marvell's specs: https://www.marvell.com/content/dam/marvell/en/public-collat... Again, those are some wild numbers if I have the correct model. Normally, Mikrotik includes switching bandwidth in their own specs, but not in this case.They are very popular and make quite good products, but as you noticed it can be tricky to find them in stock.
Besides stuff like this switch they've also produced pretty cool little micro-switches you can PoE and run as WLAN hotspots, e.g. to distance your mobile user device from some network you don't really trust, or more or less maliciously bridge a cable network through a wall because your access to the building is limited.
That switch appears to have 2x 400G ports, 2x 200G ports, 8x 50G ports, and a pair of 10G ports. So unless it allows bonding together the 50G ports (which the switch silicon probably supports at some level), it's not going to get you more than four machines connected at 200+ Gbps.
As with most 40+GbE ports, the 400Gbit ports can be split into 2x200Gbit ports with the use of special cables. So you can connect a total of 6 machines at 200Gbit.
Ah, good point. Though if splitter cables are an option, then it seems more likely that the 50G ports could be combined into a 200G cable. Marvell's product brief for that switch chip does say it's capable of operating as an 8x 200G or 4x 400G switch, but Mikrotik may need to do something on their end to enable that configuration.
You’re talking about link aggregation (LACP) here, which requires specific settings on both the switch and client machine to enable, so while it’s likely possible to combine 50Gbps ports like you describe, that’s not what I was referring to.
I'm not trolling here: Do you think that Marvell sells the chips wholesale buy the vendor buys the feature set (IP/drivers/whatever)? That would allow Marvell to effectively sell the same silicon but segment the market depending upon what buyers needs. Example: A buyer might need a config that is just a bunch of 50GB/s ports and another 100GB/s ports and another a mix. (I'm thinking about blowing fuses in the manuf phase, similar to what AMD and Intel do.) I write this as a complete noob in switching hardware.
Breakout cables typically split to 4.
e.g. QSFP28 (100GbE) splits into 4x SFP28s (25GbE each), because QSFP28 is just 4 lanes of SFP28.
Same goes for QSFP112 (400GbE). Splits into SFP112s.
It’s OSFP that can be split in half, i.e. into QSFPs.
Here’s an example of the cables I was referring to that can split a single 400Gbit QSFP56-DD port to two 200Gbit ports:
https://www.fs.com/products/101806.html
But all of this is pretty much irrelevant to my original point.
Cool! So for marginally less in cost and power usage than the numbers I quoted, you can get 2 more machines than with the RDMA setup. And you’ve still not solved the thing that I called out as the most important drawback.
how significant is the latency hit?
The OP makes reference to this with a link to a GitHub repo that has some benchmarks. TCP over Thunderbolt compared to RDMA over Thunderbolt has roughly 7-10x higher latency, ~300us vs 30-50us. I would expect TCP over 200GbE to have similar latency to TCP over Thunderbolt.
Put another way, see the graphs in the OP where he points out that the old way of clustering performs worse the more machines you add? I’d expect that to happen with 200GbE also.
And with a switch, it would likely be even worse, since the hop to the switch adds additional latency that isn’t a factor in the TB5 setup.
You're ignoring RoCE which would have the same or lower latency than RoTB. And I think macOS already supports RoCE.
MacOS does not support RoCE.
Switch probably does cut through so it starts forwarding the frame before its even fully received.
For RDMA you'd want Infiniband not Ethernet.
RDMA for new AI/HPC clusters is moving toward ethernet (the keyword to look for is RoCE). Ethernet gear is so much cheaper that you can massively over-provision to make up for some of the disadvantages of asynchronous networking, and it lets your run jobs on hyperscalers (only Azure ever supported actual IB). Most HPC is not latency-sensitive enough that it needs Infiniband’s lower jitter/median, and vendors have mostly caught up on the hardware acceleration front.
> TOTALLY okay with it consuming +600W energy
The 2019 i9 Macbook Pro has entered the chat.
Mine is to remove the extreme Macos bloat.
Hey Jeff, wherever you are: this is awesome work! I’ve wanted to try something like this for a while and was very excited for the RDMA over thunderbolt news.
But I mostly want to say thanks for everything you do. Your good vibes are deeply appreciated and you are an inspiration.
I wonder what motivates apple to release features like RDMA which are purely useful for server clusters, while ignoring basic qol stuff like remote management or rack mount hardware. It’s difficult to see it as a cohesive strategy.
Makes one wonder what apple uses for their own servers. I guess maybe they have some internal M-series server product they just haven’t bothered to release to the public, and features like this are downstream of that?
> I guess maybe they have some internal M-series server product they just haven’t bothered to release to the public, and features like this are downstream of that?
Or do they have some real server-grade product coming down the line, and are releasing this ahead of it so that 3rd party software supports it on launch day?
I worked on some of the internal server hardware. Yes they do have their own boards. Apple used to be all-in on Linux, but the newer chips are far and away more power-efficient, and power is one of the (if not the) major cost of outfitting a datacenter, at least over time.
These machines are very much internal - you can cram a lot of M-series (to use the public nomenclature) chips onto a rack-sized PCB. I was never under the impression they were destined for anything other than Apple datacenters though...
As I mentioned above, it seems to me there's a couple of feature that appeared on the customer-facing designs that were inspired by what the datacenter people wanted on their own PCB boards.
That they sell to the public? No way. They’ve clearly given up on server stuff and it makes sense for them.
That they use INTERNALLY for their servers? I could certainly see this being useful for that.
Mostly I think this is just to get money from the AI boom. They already had TB5, it’s not like this was costing them additional hardware. Just some time that probably paid off on their internal model training anyway.
> That they sell to the public? No way. They’ve clearly given up on server stuff and it makes sense for them.
Given up is not a given. A lot of the exec team has been changing.
Some people are still hoping they care for some of their older customers.
https://cottonbureau.com/p/4RUVDA/shirt/mac-pro-believe-dark...
The Mac Studio, in some ways, is in a class of its own for LLM inference. I think this is Apple leaning into that. They didn't add RDMA for general server clustering usefulness. They added it so you can put 4 Studios together in an LLM inferencing cluster exactly as demonstrated in the article.
last I heard for the private compute features they were racking and stacking m2 mac pros
I honestly forgot they still made the Mac Pro. Amazing that they have these ready to ship on their website. But at a 50% premium over similar but faster Mac Studio models, what is the point? You can't usefully put GPUs in them as far as I know. You'd have to have a different PCIe need to make it make sense.
Do they run any of their own datacenter stuff ? I thought they just outsourced to GCP
They have some of their own, also use AWS and others too.
AWS is just used for storage, because it's cheaper than Apple maintaining it, itself. Apple do have storage-datacenter at their campus at least (I've walked around one, it's many many racks of SSD's) but almost all the public stuff is on AWS (wrapped up in encryption) AFAIK.
Apple datacenters are mainly compute, other than the storage you need to run the compute efficiently.
These are my own questions - asked since the first mac mini was introduced:
- Why is the tooling so lame ?
- What do they, themselves, use internally ?
Stringing together mac minis (or a "Studio", whatever) with thunderbolt cables ... Christ.
I assume a company like Apple either has custom server boards with tons of unified memory on M series with all the i/o they could want (that are ugly and thus not productized) or just use standard expensive nvidia stuff like everyone else.
the answer is even more boring, they use GCP haha
It’s quite interesting how „boring“ (traditionally enterprise?) their backend looks on the occasional peeks you get publicly. So much Apache stuff & XML.
> rack mount hardware
I guess they prefer that third parties deal with that. There’s rack mount shelves for Mac Minis and Studios.
There's still a lot - particularly remote management, aka iLO in HP lingo - missing for an actual hands-off environment usable for hosters.
I know it’s not exactly IPMI, but don’t those little external IP KVM modules work well enough to do remote admin of Macs?
The annoying thing is there's no ability to control power (or see system metrics) outside the chassis. With servers and desktop PCs, you can usually tap into power pins and such.
I don't know what you're bemused by - there's no mystery here - you can read the release notes where it literally says this was added to support MLX:
https://developer.apple.com/documentation/macos-release-note...
Which I'm sure you saw in literally yesterday's thread about the exact same thing.
The comment is about the larger strategy surrounding that.
thunderbolt rdma is quite clearly the nuclear option for remote management.
Linux already has RDMA support but it cannot yet use Thunderbolt. It's probably quite a bit of work to add everything that's required. Is anyone working on it?
It would be great to have this for those cheap Strix Halo boxes with 128GB quad channel DDR5-8000 for using two or three of them with their 2 USB4 ports (which are Thunderbolt capable) to fit larger models.
In an ideal world, Apple would have released a Mac Pro with card slots for doing this kind of stuff.
Instead we get gimmicks over Thunderbolt.
I'd be interested in seeing numbers that split out the speed of reading input (aka prefill) and the speed of generating output (aka decode). Those numbers are usually different and I remember from this Exo article that they could be quite radically different on Mac hardware: https://blog.exolabs.net/nvidia-dgx-spark/
See https://github.com/geerlingguy/beowulf-ai-cluster/issues/17 for more data — I didn't save all the prompt processing times (Exo just outputs a time in ms, no other data for that), but will try to have another pass. Maybe also convince the Exo team to add a proper benchmarking capability ala `llama-bench` :)
or better, like you mentioned, try to convince Exo to develop in the open, so everyone gets any capability as PRs.
They are now, this morning they pushed all the code to the Exo repo, and archived the earlier Exo branch. We'll see how open they are now that whatever embargoed work they did with Apple is public..
I was impressed by the lack of dominance of Thunderbolt:
"Next I tested llama.cpp running AI models over 2.5 gigabit Ethernet versus Thunderbolt 5"
Results from that graph showed only a ~10% benefit from TB5 vs. Ethernet.
Note: The M3 studios support 10Gbps ethernet, but that wasn't tested. Instead it was tested using 2.5Gbps ethernet.
If 2.5G ethernet was only 10% slower than TB, how would 10G Ethernet have fared?
Also, TB5 has to be wired so that every CPU is connected to every other over TB, limiting you to 4 macs.
By comparison, with Ethernet, you could use a hub & spoke configuration with a Ethernet switch, theoretically letting you use more than 4 CPUs.
10G Ethernet would only marginally speed things up based on past experience with llama RPC; latency is much more helpful but still, diminishing returns with that layer split.
That’s llama, which didn’t scale nearly as well in the tests. Assumedly because it’s not optimized yet.
RDMA is always going to have lower overhead than Ethernet isn’t it?
Possibly RDMA over thunderbolt. But for RoCE (RDMA over converged Ethernet) obviously not because it's sitting on top of Ethernet. Now that could still have a higher throughput when you factor in CPU time to run custom protocols that smart NICs could just DMA instead, but the overhead is still definitively higher
The "all nodes connecting to all other nodes" setup reminds me of NUMALink, the interconnect that SGI used on many (most? all?) of their supercomputers. In an ideal configuration, each 4-socket node has two NUMALink connections to every other node. As Jeff says, it's a ton of cables, and you don't have to think of framing or congestion in the same way as with RDMA over Ethernet.
SGI's HW also had ccNUMA (cache-coherent Non-Uniform Memory Access), which, given the latencies possible in systems _physically_ spanning entire rooms, was quite a feat.
The IRIX OS even had functionality to migrate kobs and theor working memory closer to each other to lower the latency of access.
We see echoes of this when companies like high-frequency traders pay attention to motherboard layouts and co-locate and pin the PTS (proprietary trading systems) processes to specific cores based on which DIMMs are on which side of the memory controller.
The largest nodes in his cluster each have 512GB RAM. DeepSeek V3.1 is a 671B parameter model whose weights take up 700GB RAM: https://huggingface.co/deepseek-ai/DeepSeek-V3.1
I would have expected that going from one node (which can't hold the weights in RAM) to two nodes would have increased inference speed by more than the measured 32% (21.1t/s -> 27.8t/s).
With no constraint on RAM (4 nodes) the inference speed is less than 50% faster than with only 512GB.
Am I missing something?
You only get 80Gbps network bandwidth. There's your bottleneck right there. Infiniband in comparison can give you up to x10 times that.
the TB5 link (RDMA) is much slower than direct access to system memory
Weights are read-only data so they can just be memory mapped and reside on SSD (only a small fraction will be needed in VRAM at any given time), the real constraint is activations. MoE architecture should help quite a bit here.
You need all the weights every token, so even with optimal splitting the fraction of the weights you can farm out to an SSD is proportional to how fast your SSD is compared to your RAM.
You'd need to be in a weirdly compute-limited situation before you can replace significant amounts of RAM with SSD, unless I'm missing something big.
> MoE architecture should help quite a bit here.
In that you're actually using a smaller model and swapping between them less frequently, sure.
Even with MoE you still need enough memory to load all experts. For each token, only 8 experts (out of 256) are activated, but which experts are chosen changes dynamically based on the input. This means you'll be constantly loading and unloading experts from disk.
MoEs is great for distributed deployments, because you can maintain a distribution of experts that matches your workload, and you can try to saturate each expert and thereby saturate each node.
Loading and unloading data from disk is highly preferable to sending the same amount of data over a bottlenecked Thunderbolt 5 connection.
No it's not.
With a cluster of two 512GB nodes, you have to send half the weights (350GB) over a TB5 connection. But you have to do this exactly once on startup.
With a single 512GB node, you'll be loading weights from disk each time you need a different expert, potentially for each token. Depending on how many experts you're loading, you might be loading 2GB to 20GB from disk each time.
Unless you're going to shut down your computer after generating a couple of hundred tokens, the cluster wins.
> only a small fraction will be needed in VRAM at any given time
I don't think that's true. At least not without heavy performance loss in which case "just be memory mapped" is doing a lot of work here.
By that logic GPUs could run models much larger than their VRAM would otherwise allow, which doesn't seem to be the case unless heavy quantization is involved.
Existing GPU API's are sadly not conducive to this kind of memory mapping with automated swap-in. The closest thing you get AIUI is "sparse" allocations in VRAM, such that only a small fraction of your "virtual address space" equivalent is mapped to real data, and the mapping can be dynamic.
Really cool article, I liked these details that weren't exactly related to the thesis:
- the mysterious disappearance of Exo
- Jeff wants something like SMB Direct but for the Mac. Wait what? SMB Direct is a thing, wha?? I always thought networked storage was untrustworthy.
- A single M3 Ultra is fast for inference
- A framework desktop ai max 395 is only $2100
Now I have some more rabbit holes to jump down.
> You have to click buttons in the UI.
I like doing development work on a Mac, but this has to be my biggest bugbear with the system.
Very cool, I’m probably thinking too much but why are they seemingly hyping this now (I’ve seen a bunch of this recently) with no M5 Max/Ultra machines in sight. Is it because their release is imminent (I have heard Q1 2026) or is it to try and stretch out demand for M4 Max / M3 Ultra. I plan to buy one (not four) but would feel like I’m buying something that’s going to be immediately out of date if I don’t wait for the M5.
I imagine that they want to give developers time to get their RDMA support stabilized, so third party software will be ready to take advantage of RDMA when the M5 Ultra lands.
I definitely would not be buying an M3 Ultra right now on my own dime.
I am typing this on my own 512GB M3 Ultra. I've just put out some feelers for 2nd-hand sale price...
I have an M4 Max I can use to bridge any gap...
Does it actually creates a unified memory pool? it looks more like an accelerated backend for a collective communications library like nccl, which is very much not unified memory.
The yearly release cadence annoys me to no end. There is literally zero reason to have a new CPU generation every year, it just devalues Mac hardware faster.
Which I guess is the point of this for Apple, but still.
Any thoughts on the GB300 workstation with 768GB RAM (from NVIDA, Asus, Dell, ...)? Although many announcements were made it seems not to be available yet. It does have faster interconnects but will probably be much more expensive.
I wonder if there's any possibility that an RDMA expansion device could exist in the future - i.e. a box full of RAM on the other end of a thunderbolt cable. Although I guess such a device would cost almost as much as a mac mini in any case...
RDMA is not really intended for this. RDMA is really just a bunch of functionality of a PCIe device, and even PCIe isn’t really quite right to use like RAM because its cache semantics aren’t intended for this use case.
But the industry knows this, and there’s a technology that is electrically compatible with PCIe that is intended for use as RAM among other things: CXL. I wonder if a anyone will ever build CXL over USB-C.
You still need an interface which does at least two things: handles incoming read/write requests using some kind of network protocol, and operates as a memory controller for the RAM.
Texas Memory Systems was in the business of making large 'RAM Drives'. They had a product line known as "RamSan" which made many gigabytes/terabytes of DDR available via a block storage interface over infiniband and fibre channel. The control layer was implemented via FPGA.
I recall a press release from 2004 which publicized the US govt purchase of a 2.5TB RamSan. They later expanded into SSDs and were acquired by IBM in 2012.
https://en.wikipedia.org/wiki/Texas_Memory_Systems
https://www.lhcomp.com/vendors/tms/TMS-RamSan300-DataSheet.p...
https://gizmodo.com/u-s-government-purchases-worlds-largest-...
https://www.lhcomp.com/vendors/tms/TMS-RamSan20-DataSheet.pd...
https://www.ibm.com/support/pages/ibm-plans-acquire-texas-me...
Couldn't you "just" use a honking fast SSD and set it as a swap drive?
You might get close in peak bandwidth, but not in random access and latency.
Seriously, Jeff has the best job. Him and STH Patrick.
I got to spend a day with Patrick this week, and try out his massive CyPerf testing rig with multiple 800 Gbps ConnectX-8 cards!
Patrick’s enthusiasm is so contagious and you perfected tech YouTube format. There’s not a dead spot in your video.
As much as i hate Apples attitude towards hackers and modifying systems. I have to commend them for building awesome features like this
> Working with some of these huge models, I can see how AI has some use, especially if it's under my own local control. But it'll be a long time before I put much trust in what I get out of it—I treat it like I do Wikipedia. Maybe good for a jumping-off point, but don't ever let AI replace your ability to think critically!
It is a little sad that they gave someone an uber machine and this was the best he could come up with.
Question answering is interesting but not the most interesting thing one can do, especially with a home rig.
The realm of the possible
Video generation: CogVideoX at full resolution, longer clips
Mochi or Hunyuan Video with extended duration
Image generation at scale:
FLUX batch generation — 50 images simultaneously
Fine-tuning:
Actually train something — show LoRA on a 400B model, or full fine-tuning on a 70B
but I suppose "You have it for the weekend" means chatbot go brrrrr and snark
> show LoRA on a 400B model, or full fine-tuning on a 70B
Yeah, that's what I wanted to see too.
Yea, I don't understand why people use LLMs for "facts". You can get them from Wikipedia or a book.
Use them for something creative, write a short story on spec, generate images.
Or the best option: give it tools and let it actually DO something like "read my message history with my wife, find top 5 gift ideas she might have hinted at and search for options to purchase them" - perfect for a local model, there's no way in hell I'd feed my messages to a public LLM, but the one sitting next to me that I can turn off the second it twitches the wrong way? - sure.
> Yea, I don't understand why people use LLMs for "facts". You can get them from Wikipedia or a book.
Because web search is so broken these days, if you want a clean answer instead of wading through pages of SEO nonsense. It's really common (even) amongst non-techy friends that "I'll ask ChatGPT" has replaced "I'll Google it".
> That's definitely fast enough for vibe coding, if that's your thing, but it's not mine.
Why even…?
BUILD AI has a post about this and in particular sharding k-v cache across GPUs, and how network is the new memory hierarchy:
https://buildai.substack.com/p/kv-cache-sharding-and-distrib...
https://m.youtube.com/watch?v=4l4UWZGxvoc
Seems like the ecosystem is rapidly evolving
What it kinda reminds me of is PS3 cluster era. Now if I could do something similar to the minisforum..
I really hope AMD or Intel can get on the clue train and respond.
Intel in particular has half a decade of having extremely amazing Thunderbolt ports on their mobile chips, built in (alas not present on desktop chips, for shame). There's been not bad but not great thunderbolt host-to-host networking, that TCP can go over, but the system to system connectivity had been a total afterthought, not at all tuned for obvious smart readily available options like RDMA here. But nothing stops anyone from having better host-to-host protocols.
There are also so many smart good excellent next steps competitors could go for. CXL is showing up on server systems as a much lighter weight much lower latency transport that is PCIe PHY compatible but lighter weight. Adding this to consumer chips and giving even a third of a shit could blow what we see here out of the water. It could probably be done over USB4 & radically blast this bespoke RDMA capability.
Connectivity had been a bespoke special capability for too long. Intel did amazing with Xeon having integrated OmniPath 100Gb a long time ago, that was amazing, for barely any extra bucks. But the market didn't reward them kicking total ass and everyone gave up on connecting chips together. Today we are hostage to fantastically expensive shitty inefficient NIC that cost a crap ton of money to do a worse job, paying enormous penalty for not having the capability on chip, making at best asmedia io hubs do the USB4 dance a hip away from the CPU.
I really hope Intel can appreciate how good they were, see the threat of Apple kicking as here doing what Intel uniquely has been offering for half a decade with incredible Thunderbolt offerings on-chip (limited alas only to mobile chips). I hope AMD feels the heat and gets some god dMned religion and sees the pressure and thread: man they delivered so strong on PCIe lane counts but man they have been so so so slacking on io capabilities for so long, especially on consumer platforms, and Apple is using both their awesome awesome awesome on-chip memory here and their fan-tastic exceptional ability to care just even the tiniest bit about using the consumer interconnect (that already exists in hardware).
I really really really hope someone else other than Apple can ante up and care. There are so many wins to be had, so close. These companies feel so distracted from the plot. Fucking shame. Good on Apple for being the only mofos to a Tually seize the obvious that was just sitting here, they took no effort nor innovation. What a shame no other players are trying at all.
In the real world, you get a desktop PC with a bunch of GPUs connected on the same bus talking to each other.
No need for multiple computers talking over thunderbolt.
Intel is allergic for making consumer stuff good. Remember how in consumer range like half of the chips had fucking virtualisation disabled, long after competition had it on everything ?
[flagged]
Please stop.
On Intel Motherboards, it's easy to find ones that can take 2TB of RAM, for example: https://www.supermicro.com/en/products/motherboard/x14sbw-tf
This seems suboptimal.
2TB of system memory, not unified memory like Apple Silicon Macs.
The gpu can’t access that directly however. On Apple Silicon it can all be used as vram.
Wow. $40k for a friendly chat(bot)...
Hey, at least this post allows us to feel as though we spent the money ourselves.
Bravo!