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Right-sizes LLM models to your system's RAM, CPU, and GPU
- kamranjon 8mo agoThis is a great idea, but the models seem pretty outdated - it's recommending things like qwen 2.5 and starcoder 2 as perfect matches for my m4 macbook pro with 128gb of memory.
- fwipsy 8mo agoPersonally I would have found a website where you enter your hardware specs more useful.
- greggsy 8mo agoI was hoping for the same thing.
- user_7832 8mo agoSame, I opened HN on my phone and was hoping to get an idea before I booted my computer up.
- HaloZero 8mo agoYeah, installing some script to get a command line tool doesn't seem worth it.
- riidom 8mo agoThese 200 LOC install scripts turn me heavily off as well. But at least in this case, you can also just download the correct zip, extract the binary and do "./llmfit".
- spockz 8mo agoHugging Face already has this. But you need to be logged in and add the hardware to your profile.
- BloondAndDoom 8mo agoIsn’t hugging face only shows it for the model you are looking for? Is there a page that actually HF suggests a model based on your HW?
- castral 8mo agoI 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?
- andsoitis 8mo agoClaude is pretty good at among recommendations if you input your system specs.
- codazoda 8mo agoI used this prompt and it suggested a model I already have installed and one other. I'm not sure if it's the "newest" answer. > What is the best local LLM that I could run on this computer? I have Ollama (and prefer it) and I have LM Studio. I'm willing to install others, if it gives me better bang for my buck. Use bash commands to inspect the RAM and such. I prefer a model with tool calling.
- dotancohen 8mo agoIn the screenshots, each model has a use case of General, Chat, or Coding. What might be the difference between General and Chat?
- derefr 8mo ago"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-markup-language-chatml-is-important-for-a-number-of-reasons-5061f6fe2a85 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.)
- 8mo ago
- sneilan1 8mo agoThis is exactly what I needed. I've been thinking about making this tool. For running and experimenting with local models this is invaluable.
- est 8mo agoWhy do I need to download & run to checkout? Can I just submit my gear spec in some dropdowns to find out?
- esafak 8mo agoI think you could make a Github Page out of this.
- manmal 8mo agoSlightly 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.
- BoredomIsFun 8mo agoQwen 3.5 35B. FTFY.
- BloondAndDoom 8mo agoThis pretty cool, and useful but I only wish this was a website. I don’t like the idea of running an executable for something that can perfectly be done as a website. (Other than some minor features, tbh even you can enable Corsair and still check the installed models from a web browser). Sounds like a fun personal project though.
- kristopolous 8mo agoalways liked this website that kinda does something similar https://apxml.com/tools/vram-calculator https://apxml.com/tools/vram-calculator
- hhh 8mo agoHuggingface has it built in.
- azinman2 8mo agoWhere?
- hhh 8mo agoIn your preferences there is a local apps and hardware, I guess it's a little different because I just open the page of a model and it shows the hardware I've configured and shows me what quants fit.
- Twirrim 8mo agoI haven't seen a page on HF that'll show me "what models will fit", it's always model by model. The shared tool gives a list of a whole bunch of models, their respective scores, and an estimated tok/s, so you can compare and contrast. I wish it didn't require to run on the machine though. Just let me define my spec on a web page and spit out the results.
- Trigg3r 8mo agoCame across a website for this recently that may be worth a look https://whatmodelscanirun.com https://whatmodelscanirun.com
- windex 8mo agoWhat I do is i ask claude or codex to run models on ollama and test them sequentially on a bunch of tasks and rate the outputs. 30 minutes later I have a fit. It even tested the abliterated models.
- codazoda 8mo agoCan you share the prompts?
- ff00 8mo agoFound this website, not tested https://www.caniusellm.com/ https://www.caniusellm.com/
- fwipsy 8mo agoSeems broken. When I changed my auto-detected phone specs to manually entered desktop specs the recommendations didn't change at all.
- onion2k 8mo agoThat site says my 24GB M4 Pro has 8GB of VRAM. Browsers can't really detect system parameters. The Device Memory API 'anonymizes' the value returned to stop browser fingerprinting shenannigans. Interesting site, but you'll need to configure it manually for it to be accurate.
- asimovDev 8mo agoas someone who's very uneducated when it comes to LLMs I am excited about this. I am still struggling to understand correlation between system resources and context, e.g how much memory i need for N amount of context. Been recently into using local models for coding agents, mostly due to being tired of waiting for gemini to free up and it constantly retrying to get some compute time on the servers for my prompt to process like you are in the 90s being a university student and have to wait for your turn to compile your program on the university computer. Tried mistral's vibe and it would run out of context easily on a small project (not even 1k lines but multiple files and headers) at 16k or so, so I slammed it at the maximum supported in LM studio, but I wasn't sure if I was slowing it down to a halt with that or not (it did take like 10 minutes for my prompt to finish, which was 'rewrite this C codebase into C++')
- genie3io 8mo ago[dead]
- omneity 8mo agoThis is a great project. FYI all you need is the size of an LLM and the memory amount & bandwidth to know if it fits and the tok/s It’s a simple formula: llm_size = number of params * size_of_param So a 32B model in 4bit needs a minimum of 16GB ram to load. Then you calculate tok_per_s = memory_bandwidth / llm_size An RTX3090 has 960GB/s, so a 32B model (16GB vram) will produce 960/16 = 60 tok/s For an MoE the speed is mostly determined by the amount of active params not the total LLM size. Add a 10% margin to those figures to account for a number of details, but that’s roughly it. RAM use also increases with context window size.
- zozbot234 8mo ago> RAM use also increases with context window size. KV cache is very swappable since it has limited writes per generated token (whereas inference would have to write out as much as llm_active_size per token, which is way too much at scale!), so it may be possible to support long contexts with quite acceptable performance while still saving RAM. Make sure also that you're using mmap to load model parameters, especially for MoE experts. It has no detrimental effect on performance given that you have enough RAM to begin with, but it allows you to scale up gradually beyond that, at a very limited initial cost (you're only replacing a fraction of your memory_bandwidth with much lower storage_bandwidth).
- 0xbadcafebee 8mo agoWell mmap can still cause issues if you run short on RAM, and the disk access can cause latency and overall performance issues. It's better than nothing though. Agree that k/v cache is underutilized by most folks. Ollama disables Flash Attention by default, so you need to enable it. Then the Ollama default quantization for k/v cache is fp16, you can drop to q8_0 in most cases. (https://mitjamartini.com/posts/ollama-kv-cache-quantization/ https://mitjamartini.com/posts/ollama-kv-cache-quantization/) (https://smcleod.net/2024/12/bringing-k/v-context-quantisation-to-ollama/#when-not-to-use-kv-context-cache-quantisation https://smcleod.net/2024/12/bringing-k/v-context-quantisatio...)
- kittikitti 8mo agoThis is a good rule of thumb. I would also include that in most cases, RAM use exponentially increases with context window size.
- AndrewAndrewsen 8mo agoAwesome project! I recently ran a (semi-)crowdsourced quality benchmarking for models ≤20b How do you benchmark them? This would be awesome to implement at the page as well. I will link to this project at https://mlemarena.top/ https://mlemarena.top/
- mittermayr 8mo agothis is visually fantastic, but while trying this out, it says I can't run Qwen 3.5 on my machine, while it is running in the background currently, coding. So, not sure what the true value of a tool like this is other than getting a first glimpse, perhaps. Also, with unsloth providing custom adjustments, some models that are listed as undoable become doable, and they're not in the tool. Again, not trying to be harsh, it's just a really hard thing to do properly. And like many other similar tools, the maintainer here will also eventually struggle with the fact that models are popping up left and right faster than they can keep up with it.
- kittikitti 8mo agoYou might be swapping out neural weights between disk and RAM. I think people in a year or two will realize why their disks have been failing prematurely, or perhaps you too.
- api 8mo agoRead the headline and thought it rescaled LLMs down for your hardware. That would be fascinating but would degrade performance. Any work on that? Like let’s say I have 64GB memory and I want to run a 256 parameter model. At 4 bit quantized that’s 128 gigs and usually works well. 2 bits usually degrades it too much. But if you could lose data instead of precision? Would probably imply a fine tuning run afterword, so very compute intensive.
- riidom 8mo agoLM Studio has an option on model load that I believe does what you describing here: "K Cache Quantization Type" (and similar for "V"). It's marked as experimental and says the effect is basically hard to predict. Never tried myself, though.
- throwaway2027 8mo agoMore params and lower quant or higher quant and less params?
- bobmcnamara 8mo agoISA dependent quant?
- railka 8mo agoCongratulations on the launch! It's useful for Ollama users, for example. And LM Studio has built-in hints in the interface.
- 0xbadcafebee 8mo agoThis is probably catching ~85% of cases and you can possibly do better. For example, some AMD iGPUs are not covered by ROCm, so instead you rely on Vulkan support. In that case you can sometimes pass driver arguments to allow the driver to use system RAM to expand VRAM, or to specify the "correct" VRAM amount. (on iGPUs the system RAM and VRAM are physically the same thing) In this case you carefully choose how much system RAM to give up, and balance the two carefully (to avoid either OOM on one hand, or too little VRAM on the other). But do this and you can pick models that wouldn't otherwise load. Especially useful with layer offload and quantized MoE weights.
- lacoolj 8mo agoAs a few others have noted already - this should just be a website, not a CLI tool. We can easily enter our CPU, RAM, GPU specs into a form to get this info.
- minchok 8mo agoThanks, it is helpful and easy to use!
- 7777777phil 8mo agoThe "biggest model that fits" instinct is just wrong now. Compact models routinely beat massive predecessors from 12 months ago. Scaling laws only reliably predict pre-training loss anyway, not how the model actually performs on your task. Dug into the research behind this: https://philippdubach.com/posts/the-most-expensive-assumption-in-ai/ https://philippdubach.com/posts/the-most-expensive-assumptio...
- gob_blob 8mo agoThis could be a website, and it would be better as a website.