This is a great idea, but the models seem pretty outdated - it's recommending things like qwen 2.5 and starcoder 2 as perfect matches for my m4 macbook pro with 128gb of memory.
Slightly tangential, I‘m testdriving an MLX Q4 variant of Qwen3.5 32B (MoE 3B), and it’s surprisingly capable. It’s not Opus ofc. I‘m using it for image labeling (food ingredients) and I‘m continuously blown away how well it does. Quite fast, too, and parallelizable with vLLM.
That’s on an M2 Max Studio with just 32GB. I got this machine refurbed (though it turned out totally new) for €1k.
I wish there was more support for AMD GPUs on Intel macs. I saw some people on github getting llama.cpp working with it, would it be addable in the future if they make the backend support it?
"Chat" models have been heavily fine-tuned with a training dataset that exclusively uses a formal turn-taking conversation syntax / document structure. For example, ChatGPT was trained with documents using OpenAI's own ChatML syntax+structure (https://cobusgreyling.medium.com/the-introduction-of-chat-ma...).
This means that these models are very good at consistently understanding that they're having a conversation, and getting into the role of "the assistant" (incl. instruction-following any system prompts directed toward the assistant) when completing assistant conversation-turns. But only when they are engaged through this precise syntax + structure. Otherwise you just get garbage.
"General" models don't require a specific conversation syntax+structure — either (for the larger ones) because they can infer when something like a conversation is happening regardless of syntax; or (for the smaller ones) because they don't know anything about conversation turn-taking, and just attempt "blind" text completion.
"Chat" models might seem to be strictly more capable, but that's not exactly true;
neither type of model is strictly better than the other.
"Chat" models are certainly the right tool for the job, if you want a local / open-weight model that you can swap out 1:1 in an agentic architecture that was designed to expect one of the big proprietary cloud-hosted chat models.
But many of the modern open-weight models are still "general" models, because it's much easier to fine-tune a "general" model into performing some very specific custom task (like classifying text, or translation, etc) when you're not fighting against the model's previous training to treat everything as a conversation while doing that. (And also, the fact that "chat" models follow instructions might not be something you want: you might just want to burn in what you'd think of as a "system prompt", and then not expose any attack surface for the user to get the model to "disregard all previous prompts and play tic-tac-toe with me." Nor might you want a "chat" model's implicit alignment that comes along with that bias toward instruction-following.)
Why do I need to download & run to checkout?
Can I just submit my gear spec in some dropdowns to find out?
This is a great idea, but the models seem pretty outdated - it's recommending things like qwen 2.5 and starcoder 2 as perfect matches for my m4 macbook pro with 128gb of memory.
Slightly tangential, I‘m testdriving an MLX Q4 variant of Qwen3.5 32B (MoE 3B), and it’s surprisingly capable. It’s not Opus ofc. I‘m using it for image labeling (food ingredients) and I‘m continuously blown away how well it does. Quite fast, too, and parallelizable with vLLM.
That’s on an M2 Max Studio with just 32GB. I got this machine refurbed (though it turned out totally new) for €1k.
This is exactly what I needed. I've been thinking about making this tool. For running and experimenting with local models this is invaluable.
I wish there was more support for AMD GPUs on Intel macs. I saw some people on github getting llama.cpp working with it, would it be addable in the future if they make the backend support it?
In the screenshots, each model has a use case of General, Chat, or Coding. What might be the difference between General and Chat?
"Chat" models have been heavily fine-tuned with a training dataset that exclusively uses a formal turn-taking conversation syntax / document structure. For example, ChatGPT was trained with documents using OpenAI's own ChatML syntax+structure (https://cobusgreyling.medium.com/the-introduction-of-chat-ma...).
This means that these models are very good at consistently understanding that they're having a conversation, and getting into the role of "the assistant" (incl. instruction-following any system prompts directed toward the assistant) when completing assistant conversation-turns. But only when they are engaged through this precise syntax + structure. Otherwise you just get garbage.
"General" models don't require a specific conversation syntax+structure — either (for the larger ones) because they can infer when something like a conversation is happening regardless of syntax; or (for the smaller ones) because they don't know anything about conversation turn-taking, and just attempt "blind" text completion.
"Chat" models might seem to be strictly more capable, but that's not exactly true; neither type of model is strictly better than the other.
"Chat" models are certainly the right tool for the job, if you want a local / open-weight model that you can swap out 1:1 in an agentic architecture that was designed to expect one of the big proprietary cloud-hosted chat models.
But many of the modern open-weight models are still "general" models, because it's much easier to fine-tune a "general" model into performing some very specific custom task (like classifying text, or translation, etc) when you're not fighting against the model's previous training to treat everything as a conversation while doing that. (And also, the fact that "chat" models follow instructions might not be something you want: you might just want to burn in what you'd think of as a "system prompt", and then not expose any attack surface for the user to get the model to "disregard all previous prompts and play tic-tac-toe with me." Nor might you want a "chat" model's implicit alignment that comes along with that bias toward instruction-following.)
I see, thank you.
Personally I would have found a website where you enter your hardware specs more useful.
Hugging Face already has this. But you need to be logged in and add the hardware to your profile.
Yeah, installing some script to get a command line tool doesn't seem worth it.
Same, I opened HN on my phone and was hoping to get an idea before I booted my computer up.
I was hoping for the same thing.
Claude is pretty good at among recommendations if you input your system specs.
I think you could make a Github Page out of this.