A few lessons learned:
1. small models like the new qwen3.5:9b can be fantastic for local tool use, information extraction, and many other embedded applications.
2. For coding tools, just use Google Antigravity and gemini-cli, or, Anthropic Claude, or...
Now to be clear, I have spent perhaps 100 hours in the last year configuring local models for coding using Emacs, Claude Code (configured for local), etc. However, I am retired and this time was a lot of fun for me: lot's of efforts trying to maximize local only results. I don't recommend it for others.
I do recommend getting very good at using embedded local models in small practical applications. Sweet spot.
What's also new here, is VRAM-context size trade-off: for 25% of it's attention network, they use the regular KV cache for global coherency, but for 75% they use a new KV cache with linear(!!!!) memory-token-context size expansion! which means, eg ~100K token -> 1.5gb VRAM use -meaning for the first time you can do extremely long conversations / document processing with eg a 3060.
Strong, strong recommend.
I like the idea of finding practical uses for it, but so far haven't managed to be creative enough. I'm so accustomed to using these things for programming.
Example: “what is the air speed velocity of a swallow?” - qwen knew it was a Monty Python gag, but couldnt and didnt figure out which one.
I'd also be curious to see if people have started doing censorship analysis of various models, like Qwen differing Tiananmen square to government documments while Llama straights up answers the question.
If you want a general knowledge model for answering questions or a coding agent, nothing you can run on your MacBook will come close to the frontier models. It's going to be frustrating if you try to use local models that way. But there are a lot of useful applications for local-sized models when it comes to interpreting and transforming unstructured data.
An OpenRunPod with decent usage might encourage more non-leading labs to dump foundation models into the commons. We just need infra to run it. Distilling them down to desktop is a fool's errand. They're meant to run on DC compute.
I'm fine with running everything in the cloud as long as we own the software infra and the weights.
This is conceivably the only way we could catch up to Claude Code is to have the Chinese start releasing their best coding models and for them to get significant traction with companies calling out to hosted versions. Otherwise, we're going to be stuck in a take off scenario with no bridge.
Everything worked fine on GPT but Qwen as often as not preferred to pretend to call a tool and not actually call it. After much aggravation I wound up just setting my bot / llama swap to use gpt for chat and only load up qwen when someone posts an image and just process / respond to the image with qwen and pop back over to gpt when the next chat comes in.
Instead you should give it tools to search over the mailbox for terms, labels, addresses, etc. so that the model can do fine grained filters based on the query, read the relevant emails it finds, then answer the question.
The local models are quite weak here.
(In terms of intelligence, they tend to score similarly to a dense model that's as big as the geometric mean of the full model size and the active parameters, i.e. for GPT-OSS-20B, it's roughly as smart as a sqrt(20b*3.6b) ≈ 8.5b dense model, but produces tokens 2x faster.)
For MoE models, it should be using the active parameters in memory bandwidth computation, not the total parameters.
> A Mixture of Experts model splits its parameters into groups called "experts." On each token, only a few experts are active — for example, Mixtral 8x7B has 46.7B total parameters but only activates ~12.9B per token. This means you get the quality of a larger model with the speed of a smaller one. The tradeoff: the full model still needs to fit in memory, even though only part of it runs at inference time.
> A dense model activates all its parameters for every token — what you see is what you get. A MoE model has more total parameters but only uses a subset per token. Dense models are simpler and more predictable in terms of memory/speed. MoE models can punch above their weight in quality but need more VRAM than their active parameter count suggests.
> GPT-OSS-20B has 3.6B active parameters, so it should perform similarly to a 3-4B dense model, while requiring enough VRAM to fit the whole 20B model.
First, the token generation speed is going to be comparable, but not the prefil speed (context processing is going to be much slower on a big MoE than on a small dense model).
Second, without speculative decoding, it is correct to say that a small dense model and a bigger MoE with the same amount of active parameters are going to be roughly as fast. But if you use a small dense model you will see token generation performance improvements with speculative decoding (up to x3 the speed), whereas you probably won't gain much from speculative decoding on a MoE model (because two consecutive tokens won't trigger the same “experts”, so you'd need to load more weight to the compute units, using more bandwidth).
So by doing so, this calculator is telling you that you should be running entirely dense models, and sparse MoE models that maybe both faster and perform better are not recommended.
"What is the highest-quality model that I can run on my hardware, with tok/s greater than <x>, and context limit greater than <y>"
(My personal approach has just devolved into guess-and-check, which is time consuming.) When using TFA/llmfit, I am immediately skeptical because I already know that Qwen 3.5 27B Q6 @ 100k context works great on my machine, but it's buried behind relatively obsolete suggestions like the Qwen 2.5 series.
I'm assuming this is because the tok/s is much higher, but I don't really get much marginal utility out of tok/s speeds beyond ~50 t/s, and there's no way to sort results by quality.
"what is the best open weight model for high-quality coding that fits in 8GB VRAM and 32GB system RAM with t/s >= 30 and context >= 32768" -> Qwen2.5-Coder-7B-Instruct
"what is the best open weight model for research w/web search that fits in 24GB VRAM and 32GB system RAM with t/s >= 60 and context >= 400k" -> Qwen3-30B-A3B-Instruct-2507
"what is the best open weight embedding model for RAG on a collection of 100,000 documents that fits in 40GB VRAM and 128GB system RAM with t/s >= 50 and context >= 200k" -> Qwen3-Embedding-8B
Just to be clear, it may sound like a snarky comment but I'm really curious from you or others how do you see it. I mean there are some batches long running tasks where ignoring electricity it's kind of free but usually local generation is slower (and worse quality) and we all kind of want some stuff to get done.
Or is it not about the cost at all, just about not pushing your data into the clouds.
Nevertheless, I spend a lot of time with local models because of:
1. Pure engineering/academic curiosity. It's a blast to experiment with low-level settings/finetunes/lora's/etc. (I have a Cog Sci/ML/software eng background.)
2. I prefer not to share my data with 3rd party services, and it's also nice to not have to worry too much about accidentally pasting sensitive data into prompts (like personal health notes), or if I'm wasting $ with silly experiments, or if I'm accidentally poisoning some stateful cross-session 'memories' linked to an account.
3. It's nice to be able solve simple tasks without having to reason about any external 'side-effects' outside my machine.
on data i would never ever want to upload to any vendor if i can avoid it
Well, granted my project is trying to do this in a way that works across multiple devices and supports multiple models to find the best “quality” and the best allocation. And this puts an exponential over the project.
But “quality” is the hard part. In this case I’m just choosing the largest quants.
I wouldn't expect a perfect single measurement of "quality" to exist, but it seems like it could be approximated enough to at least be directionally useful. (e.g. comparing subsequent releases of the same model family)
- If you already HAVE a computer and are looking for models: LLMFit
- If you are looking to BUY a computer/hardware, and want to compare/contrast for local LLM usage: This
You cannot exactly run LLMFit on hardware you don't have.
It looks like I can run more local LLMs than I thought, I'll have to give some of those a try. I have decent memory (96GB) but my M2 Max MBP is a few years old now and I figured it would be getting inadequate for the latest models. But llmfit thinks it's a really good fit for the vast majority of them. Interesting!
It says I have an Arc 750 with 2 GB of shared RAM, because that's the GPU that renders my browser...but I actually have an RTX1000 Ada with 6 GB of GDDR6. It's kind of like an RTX 4050 (not listed in the dropdowns) with lower thermal limits. I also have 64 GB of LPDDR5 main memory.
It works - Qwen3 Coder Next, Devstral Small, Qwen3.5 4B, and others can run locally on my laptop in near real-time. They're not quite as good as the latest models, and I've tried some bigger ones (up to 24GB, it produces tokens about half as fast as I can type...which is disappointingly slow) that are slower but smarter.
But I don't run out of tokens.
When I visit the site with an Apple M1 Max with 32GB RAM, the first model that's listed is Llama 3.1 8B, which is listed as needing 4.1GB RAM.
But the weights for Llama 3.1 8B are over 16GB. You can see that here in the official HF repo: https://huggingface.co/meta-llama/Llama-3.1-8B/tree/main
The model this site calls 'Llama 3.1 8B' is actually a 4-bit quantized version ( Q4_K_M) available on ollama.com/library: https://ollama.com/library/llama3.1:8b
If you're going to recommend a model to someone based on their hardware, you have to recommend not only a specific model, but a specific version of that model (either the original, or some specific quantized version).
This matters because different quantized versions of the model will have different RAM requirements and different performance characteristics.
Another thing I don't like is that the model names are sometimes misleading. For example, there's a model with the name 'DeepSeek R1 1.5B'. There's only one architecture for DeepSeek R1, and it has 671B parameters. The model they call 'DeepSeek R1 1.5B' does not use that architecture. It's a qwen2 1.5B model that's been finetuned on DeepSeek R1's outputs. (And it's a Q4_K_M quantized version.)
A couple suggestions:
1. I have an M3 Ultra with 256GB of memory, but the options list only goes up to 192GB. The M3 Ultra supports up to 512GB. 2. It'd be great if I could flip this around and choose a model, and then see the performance for all the different processors. Would help making buying decisions!
Im sorry but spending this kind of money when you could have just built yourself a dual 3090 workstation that would have been better for pretty much everything including local models is just plain stupid.
Hell, even one 3090 can now run Gemma 3 27b qat very fast.
Doesn't run macOS
Android: https://play.google.com/store/apps/details?id=com.coticsy.ll...
Website : https://coticsy.com/aime.html
I wish the website could tell us how life is like in 2027!
Which in this case Im thankful that Apple isn't too keen on following standards like these.
Nano Banana Pro for anything image and video related.
Grok Imagine for pretty decent porn generation.
Super impressive comparisons, and correlates with my perception having three seperate generations of GPU (from your list pulldown). Thanks for including the "old AMD" Polaris chipsets, which are actually still much faster than lower-spec Apple silicon. I have Ollama3.1 on a VEGA64 and it really is twice as fast as an M2Pro...
----
For anybody that thinks installing a local LLM is complicated: it's not (so long as you have more than one computer, don't tinker on your primary workhorse). I am a blue collar electrician (admittedly: geeky); no more difficult than installing linux. I used an online LLM to help me install both =D
The website is super useful. That theme though... low-contrast text on too-dark theme is, uh, barely readable for me.
Love the idea though!
EDIT: Okay the whole thing is nonsense and just some rough guesswork or asking an LLM to estimate the values. You should have real data (I'm sure people here can help) and put ESTIMATE next to any of the combinations you are guessing.
Preliminary testing did not come to that conclusion.
> Apple’s New M5 Max Changes the Local AI Story
For the lazy: that's less then 3x: 1.8 * 3 = 5.4
Its using WebGPU as a proxy to estimate system resource. Chrome tends to leverage as much resources (Compute + Memory) as the OS makes available. Safari tends to be more efficient.
Maybe this was obvious to everyone else. But its worth re-iterating for those of us skimmers of HN :)
Currently, Nemotron 3 Super using Unsloth's UD Q4_K_XL quant is running nearly everything I do locally (replacing Qwen3.5 122b)
ive been working with quite a few open weight models for the last year and especially for things like images, models from 6 months would return garbage data quickly, but these days qwen 3.5 is incredible, even the 9b model.
But yes, if there is a choice I want quality over speed. At same quality, I definitely want speed.
In reality, gpt-oss-120b fits great on the machine with plenty of room to spare and easily runs inference north of 50 t/s depending on context.
At the moment I'm exploring:
- nightmedia/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-qx64-hi-mlx
- BeastCode/Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-4bit
- mlx-community/Qwen3-Coder-Next-4bit
- The t/s estimation per machine is off. Some of these models run generation at twice the speed listed (I just checked on a couple macs & an AMD laptop). I guess there's no way around that, but some sort of sliding scale might be better.
- Ollama vs Llama.cpp vs others produce different results. I can run gpt-oss 20b with Ollama on a 16GB Mac, but it fails with "out of memory" with the latest llama.cpp (regardless of param tuning, using their mxfp4). Otoh, when llama.cpp does work, you can usually tweak it to be faster, if you learn the secret arts (like offloading only specific MoE tensors). So the t/s rating is even more subjective than just the hardware.
- It's great that they list speed and size per-quant, but that needs to be a filter for the main list. It might be "16 t/s" at Q4, but if it's a small model you need higher quant (Q5/6/8) to not lose quality, so the advertised t/s should be one of those
- Why is there an initial section which is all "performs poorly", and then "all models" below it shows a ton of models that perform well?
The model is not great, but it was the "least amount of setup" LLM I could run on someone else's machine.
Including structured output, but has a tiny context window I could use.
It would be useful to filter which model to use based on the objective or usage (i.e., for data extraction vs. coding).
Also, just looking at VRAM kind of misses that a lot of CPU memory can be shared with the GPU via layer offloading. I think there is ultimately a need for a native client, like a CPU/GPU benchmark, to figure out how the model will actually perform more precisely.
I don't really understand how the interface to the NPU chip looks from the perspective of a non-system caller, if it exists at all. This is a Samsung device but I am wondering about the general principle.
Then it shows the full resolution models, which are completely unnecessary to run quality inference. Quantized models are routine for local inference and it should realize that.
Needs work.
There's so many knobs to tweak, it's a non trivial problem
- Average/median length of your Prompts
- prompt eval speed (tok/s)
- token generation speed (tok/s)
- Image/media encoding speed for vision tasks
- Total amount of RAM
- Max bandwidth of ram (ddr4, ddr5, etc.?)
- Total amount of VRAM
- "-ngl" (amount of layers offloaded to GPU)
- Context size needed (you may need sub 16k for OCR tasks for instance)
- Size of billion parameters
- Size of active billion parameters for MoE
- Acceptable level of Perplexity for your use case(s)
- How aggressive Quantization you're willing to accept (to maintain low enough perplexity)
- even finer grain knobs: temperature, penalties etc.
Also, Tok/s as a metric isn't enough then because there's:
- thinking vs non-thinking: which mode do you need?
- models that are much more "chatty" than others in the same area (i remember testing few models that max out my modest desktop specs, qwen 2.5 non-thinking was so much faster than equivalent ministral non-thinking even though they had equivalent tok/s... Qwen would respond to the point quickly)
At the end, final questions are: are you satisfied with how long getting an answer took? and was the answer good enough?
The same exercise with paid APIs exists too, obviously less knobs but depending on your use case, there's still differences between providers and models. You can abstract away a lot of the knobs , just add "are you satisfied with how much it cost" on top of the other 2 questions
The size of the quantization you chose also makes a difference.
The GPU driver also plays an important role.
What was your approach? What software did you use to run the models?
It pretty obvious that this reasoning scaling is a mirage, parameters are all you need. Everything else is mostly just wasting time while hardware get better.
My $3k Macbook can run `GPT-OSS 20B` at ~16 tok/s according to this guide.
Or I can run `GPT-OSS 120B` (a 6X larger model) at 360 tok/s (30X faster) on Groq at $0.60/Mtok output tokens.
To generate $3k worth of output tokens on my local Mac at that pricing it would have to run 10 years continuously without stopping.
There's virtually no economic break-even to running local models, and no advantage in intelligence or speed. The only thing you really get is privacy and offline access.
Instead if you wanted to get a macbook anyway, you get to run local models for free on top. Very different story.
There are quite a few of them but their marketing is just confusing and full of buzz words. I've been tinkering with OpenRouter that acts as a middleman.
Gemini api use also comes with a free tier.
Wait 5-10 minutes, and should be done.
It genuinely is that simple.
You can even use local models using claude code or codex infrastrucutre (MASSIVE UNLOCK), but you need solid GPU(s) to run decent models. So that's the downside.
I would've thought no, because of the knowledge cutoff in whatever model you use to download it.
- Which models in the list are the best for my selected task? (If you don't track these things regularly, the list is a little overwhelming.) Sorting by various benchmark scores might be useful?
- How much more system resources do I need to run the models currently listed at F, D or C at B, A, or S-tier levels? (Perhaps if you hover over the score, it could tell you?)
Quick, someone go vibe code that.
Right now we started experimenting with 2 H100's, 160GB models. But even a single one is wide out of anyone others league.
Could you please add title="explanation" over each selected item at the top. For example, when I choose my video card the ram changes... I'm not sure if the RAM selection is GPU RAM? The GRAM was already listed with the graphics card. SO I choose 96GB which is my Main memory? And the GB/s I am assuming it's GPU -> CPU speed?
Radeon VII
https://www.amd.com/en/support/downloads/drivers.html/graphi...
Even when running locally, the model often starts structured but gradually becomes more verbose or explanatory in longer threads.
Curious if others have seen similar behavior when using local setups.
[1]: https://github.com/ggml-org/llama.cpp/blob/master/docs/specu...
Since I considered buying M3 Ultra and feel like it the most often discussed regarding using Apple hardware for runninh local LLMs. Where speed might be okay, but prompt processing can take ages.
One thing I do wonder is what sort of solutions there are for running your own model, but using it from a different machine. I don't necessarily want to run the model on the machine I'm also working from.
You can also use the kubernetes operator to run them on a cluster: https://ollama-operator.ayaka.io/pages/en/
I also want to run vision like Yocto and basic LLM with TTS/STT
"I can run a model" is mildly interesting. I can run OSS-20B on my M1 Pro. It works, I tried it, just I don't find any application.
The tool is very nice though.
Just ask any Apple user, they don't actually use local models.
Not sure if it still works.
Just FYI.
The website says that code export is not working yet.
That’s a very strange way to advertise yourself.
I’d like to be able to use a local model (which one?) to power Copilot in vscode, and run coding agent(s) (not general purpose OpenClaw-like agents) on my M2 MacBook. I know it’ll be slow.
I suspect this is actually fairly easy to set up - if you know how.
You're probably not going to get anything working well as an agent on an M2 MacBook, but smaller models do surprisingly well for focused autocomplete. Maybe the Qwen3.5 9B model would run decently on your system?
https://unsloth.ai/docs/models/qwen3.5 - running locally guide for the Qwen 3.5 family of models, which have a range of different sizes.
It's not as bad as you might think to compile llama.cpp for your target architecture and spin up an OpenAI compatible API endpoint. It even downloads the models for you.
This isn’t nearly complete.
2. Add a 150% size bonus to your site.
Otherwise, cool site, bookmarked.
What's your experience with qwen3.5 for debugging tasks? I've mostly stuck with the big models so far.
It’s basically an open-source OS layer that standardizes the local AI stack—Kubernetes (K3s) for orchestration, standardized model serving, and GPU scheduling. The goal is to stop fiddling with Python environments/drivers and just treat local agents like standardized containers. It runs on Mac Minis or dedicated hardware.