This is more of an ad, not a review, and reads like the author has hardly any experience with the things he's trying out. That Z Image Turbo diffusion model would've also run on many consumer GPUs and with way higher performance for a fraction of the price. Misleading.
Highly recommend lemonade server if you have a strix halo desktop. Been using Qwen3.6-35B @ Q_8 as my main driver and it’s been great with 60 TPS for generation. I occasionally use the 27B @ q6 but only get 20-25 TPS for generation with MTP.
I used llama swap for a while before leaning into lemonade. The UI has improved a lot, but be careful as the most of the models default to very small 4K context windows by default.
They’re doing some nice things with their Halo models, which load an ensemble of different types of model at the same time. With high vram it’s easy to keep them all in memory, so even though the compute is limited the context switching is fast.
You do lag a bit on the upstream engine releases, the llama.cpp/sd.cpp/whisper libraries are downloaded from inside the app.
vLLM is in experimental mode, I haven’t tested it. It’s limited in the models they suggest, but you can download anything from huggingface with a two click install.
Unfortunately, the table of models and tokens per second (TPS) and time to first token (TTFT) is not helpful without specifying the quantization of the model.
Does anyone else feel like it would be great to be able to purchase $4000 AI box but 128 gigs is not enough. If I spend all that money and it doesn’t really do what I wanted to do, whats the point?
It’s kind of like general aviation where you can go buy a Cessna but it’s only going to realistically get you somewhere you could drive anyways but do you really wanna spend that mush cash to get road trip distance at slightly better than road trip speeds? You really need a 5 million dollar jet and that’s just not practical. That’s sort of how I feel about this device.
I own one, I don’t feel like the RAM is a huge issue (of course I want 192GB to run something like DS4 Flash). The lack of FP4 and slow memory bandwidth is rough. NVFP4 support is such a huge advantage that I would recommend others to buy a DGX spark over a strix halo if you’re using it purely for AI. Strix halo works better for general computing.
The Strix Halo is a great dev machine and a mediocre AI machine. You can run Qwen 3.6 27B at a decent speed, or larger MoE models, and that's about it. For some that's more than enough though, myself included.
We're maybe only 2 years away from really useful, relatively affordable local LLM usage. You can buy a 5090 PC for $5-6k but 32GB of VRAM really limits model sizes to ~31B. And that won't change (even with NVidia's next generation) because NVidia uses VARM as an aggressive market segmentation technique.
No, the hope really is these other platforms with a shared memory architecture. The DGX Spark won't be it because of the aforementioned market segmentation. So that leaves two players: AMD and Apple.
The AMD platform is still too low memory bandwidth, currently <300GB/s. For comparison a 5090 (or 6000 Pro) is 1.8TB/s and the M3 Ultra Mac Studio is ~900GB/s. Oh and B100/B200 uses HBM3e memory at ~3.2TB/s. The M5 Max in some Macbook Pros tops out at ~600GB/s. So you need access to better RAM and better CPU architecture for all this.
My great white hope is Apple. They have the market power to get memory and build silicon that coul dhave enough FLOPS to compete with NVidia's platform. They've started talking about it and I've seen rumors they're targeting the M7 generation (2028) for a huge leap. I'll believe it when I see it however.
But the point is, I think we'll be running 31B models at 100+tok/s on enthusiast hardware in 2 years and we'll likely be able to locally run 100-400B models, possibly larger.
Until RAM prices drop and can economically get machines with 256GB, 512GB and higher bandwidth... I frankly think the local AI story is going to be still fairly muted for most people.
My Spark can do Qwen3.6 MoE A3B at 60 to 70-ish token/second and that's really good, but there's limits the usefulness of that model. It's not useful for coding, in any case.
Once people can run something like GLM 5.2 at lower quants (512GB could do a passable job), then I think the story changes.
Whether we ever see DRAM as cheap as it was ever again, I don't know.
Agree - the 128GB Strix Halo is capable if you use LLMs as assistants, but it's not so good if you use LLMs as agents (or worse, agent teams/swarms) since all of the models that can fit on it are pretty dumb compared to frontier or near-frontier models. You can at best hope for Sonnet-level capabilities.
That doesn't mean that local models are useless though! If Mythos/Sol is an ASI that threatens to take your job and turn you into paperclips, then Qwen/Gemma is an old-fashioned office secretary that loyally helps you with tasks but doesn't have a good grasp of details. Every white-collar worker 50 years ago would have killed to have a hard-working personal secretary.
Part of me wonders, would 3d xpoint (if still around) be a viable option?
Yeah it is slower than real RAM by a good amount for latency, but you can get similar bandwidth and the cost was history about half of the same size DDR.
Possibly - I've heard anecdotal reports that old Intel Optane chips are in hot demand right now. Intel/Micron probably would have made a killing if they had kept that product line alive for a few more years. Never miss an attempt to snatch defeat from the jaws of victory!
I was thought experimenting the other day... ~10 nVME drives striped and running parallel could approximate the memory bandwidth of DDR5 DRAM in a box like this. Like you say, latency wouldn't compete but on raw throughput would be comparable.
Not anymore cost effective, I guess, but gets you the ability to work over very large model sizes maybe. But the problem is that tensor matmul etc hardware wouldn't work effectively with it.
This is more of an ad, not a review, and reads like the author has hardly any experience with the things he's trying out. That Z Image Turbo diffusion model would've also run on many consumer GPUs and with way higher performance for a fraction of the price. Misleading.
It's Microcenter. Don't get your reviews from a retailer.
Highly recommend lemonade server if you have a strix halo desktop. Been using Qwen3.6-35B @ Q_8 as my main driver and it’s been great with 60 TPS for generation. I occasionally use the 27B @ q6 but only get 20-25 TPS for generation with MTP.
I second this.
I used llama swap for a while before leaning into lemonade. The UI has improved a lot, but be careful as the most of the models default to very small 4K context windows by default.
They’re doing some nice things with their Halo models, which load an ensemble of different types of model at the same time. With high vram it’s easy to keep them all in memory, so even though the compute is limited the context switching is fast.
You do lag a bit on the upstream engine releases, the llama.cpp/sd.cpp/whisper libraries are downloaded from inside the app.
vLLM is in experimental mode, I haven’t tested it. It’s limited in the models they suggest, but you can download anything from huggingface with a two click install.
Unfortunately, the table of models and tokens per second (TPS) and time to first token (TTFT) is not helpful without specifying the quantization of the model.
Does anyone else feel like it would be great to be able to purchase $4000 AI box but 128 gigs is not enough. If I spend all that money and it doesn’t really do what I wanted to do, whats the point?
It’s kind of like general aviation where you can go buy a Cessna but it’s only going to realistically get you somewhere you could drive anyways but do you really wanna spend that mush cash to get road trip distance at slightly better than road trip speeds? You really need a 5 million dollar jet and that’s just not practical. That’s sort of how I feel about this device.
I own one, I don’t feel like the RAM is a huge issue (of course I want 192GB to run something like DS4 Flash). The lack of FP4 and slow memory bandwidth is rough. NVFP4 support is such a huge advantage that I would recommend others to buy a DGX spark over a strix halo if you’re using it purely for AI. Strix halo works better for general computing.
There's a glimmer of hope with ROCmFP4 which seems to double the current throughput: https://github.com/charlie12345/rocmfp4-llama
The Strix Halo is a great dev machine and a mediocre AI machine. You can run Qwen 3.6 27B at a decent speed, or larger MoE models, and that's about it. For some that's more than enough though, myself included.
seems like an absolute amateur wrote this article
We're maybe only 2 years away from really useful, relatively affordable local LLM usage. You can buy a 5090 PC for $5-6k but 32GB of VRAM really limits model sizes to ~31B. And that won't change (even with NVidia's next generation) because NVidia uses VARM as an aggressive market segmentation technique.
No, the hope really is these other platforms with a shared memory architecture. The DGX Spark won't be it because of the aforementioned market segmentation. So that leaves two players: AMD and Apple.
The AMD platform is still too low memory bandwidth, currently <300GB/s. For comparison a 5090 (or 6000 Pro) is 1.8TB/s and the M3 Ultra Mac Studio is ~900GB/s. Oh and B100/B200 uses HBM3e memory at ~3.2TB/s. The M5 Max in some Macbook Pros tops out at ~600GB/s. So you need access to better RAM and better CPU architecture for all this.
My great white hope is Apple. They have the market power to get memory and build silicon that coul dhave enough FLOPS to compete with NVidia's platform. They've started talking about it and I've seen rumors they're targeting the M7 generation (2028) for a huge leap. I'll believe it when I see it however.
But the point is, I think we'll be running 31B models at 100+tok/s on enthusiast hardware in 2 years and we'll likely be able to locally run 100-400B models, possibly larger.
Until RAM prices drop and can economically get machines with 256GB, 512GB and higher bandwidth... I frankly think the local AI story is going to be still fairly muted for most people.
My Spark can do Qwen3.6 MoE A3B at 60 to 70-ish token/second and that's really good, but there's limits the usefulness of that model. It's not useful for coding, in any case.
Once people can run something like GLM 5.2 at lower quants (512GB could do a passable job), then I think the story changes.
Whether we ever see DRAM as cheap as it was ever again, I don't know.
Agree - the 128GB Strix Halo is capable if you use LLMs as assistants, but it's not so good if you use LLMs as agents (or worse, agent teams/swarms) since all of the models that can fit on it are pretty dumb compared to frontier or near-frontier models. You can at best hope for Sonnet-level capabilities.
That doesn't mean that local models are useless though! If Mythos/Sol is an ASI that threatens to take your job and turn you into paperclips, then Qwen/Gemma is an old-fashioned office secretary that loyally helps you with tasks but doesn't have a good grasp of details. Every white-collar worker 50 years ago would have killed to have a hard-working personal secretary.
Part of me wonders, would 3d xpoint (if still around) be a viable option?
Yeah it is slower than real RAM by a good amount for latency, but you can get similar bandwidth and the cost was history about half of the same size DDR.
Possibly - I've heard anecdotal reports that old Intel Optane chips are in hot demand right now. Intel/Micron probably would have made a killing if they had kept that product line alive for a few more years. Never miss an attempt to snatch defeat from the jaws of victory!
I was thought experimenting the other day... ~10 nVME drives striped and running parallel could approximate the memory bandwidth of DDR5 DRAM in a box like this. Like you say, latency wouldn't compete but on raw throughput would be comparable.
Not anymore cost effective, I guess, but gets you the ability to work over very large model sizes maybe. But the problem is that tensor matmul etc hardware wouldn't work effectively with it.
Useful for KVCache though.