This seems to be estimating based on memory bandwidth / size of model, which is a really good estimate for dense models, but MoE models like GPT-OSS-20b don't involve the entire model for every token, so they can produce more tokens/second on the same hardware. 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.
(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.)
When running models on my phone - either through the web browser or via an app - is there any chance it uses the phone's NPU, or will these be GPU only?
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.
tbh i stopped caring about "can i run X locally" a while ago. for anything where quality matters (scripting, code, complex reasoning) the local models are just not there yet compared to API. where local shines is specific narrow tasks - TTS, embeddings, whisper for STT, stuff like that. trying to run a 70b model at 3 tok/s on your gaming GPU when you could just hit an API for like $0.002/req feels like a weird flex IMO
"Available to userspace" is a much different thing than "available to every website that wants it, even in private mode".
I too was a little surprised by this. My browser (Vivladi) makes a big deal about how privacy-conscious they are, but apparently browser fingerprinting is not on their radar.
This lacks a whole lot of mobile GPUs. It also does not understand that you can share CPU memory with the GPU, or perform various KV cache offloading strategies to work around memory limits.
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.
Having the rating of how well the model will run for you is cool. I miss to also have some rating of the model capabilities (even if this is tricky). There are way too many to choose. And just looking at the parameter number or the used memory is not always a good indication of actual performance.
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!
The RAM/VRAM cutoff matters more than the parameter count alone. A 13B model in Q4_K_M quantization fits in 8GB VRAM with reasonable throughput, but the same model in fp16 needs 26GB. Most calculators treat quantization as a footnote when it is actually the primary variable. The question is not "can I run 13B" but "what quantization level gives acceptable quality at my hardware ceiling".
i think the perplexity is more important than tokens per second. tokens per second is relatively useless in my opinion. there is nothing worse than getting bad results returned to you very quickly and confidently.
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.
This is awesome, it would be great to cross reference some intelligence benchmarks so that I can understand the trade off between RAM consumption, token rate and how good the model is
For some reason it doesn't react to changing the RAM amount in the combo box at the top. If I open this on my Ryzen AI Max 395+ with 32 GB of unified memory, it thinks nothing will fit because I've set it up to reserve 512MB of RAM for the GPU.
This doesn't look accurate to me. I have an RX9070 and I've been messing around with Qwen 3.5 35B-A3B. According to this site I can't even run it, yet I'm getting 32tok/s ^.-
Yeah, that's weird, seems it has later models, and earlier, but specifically not Pro 6000? Also, based on my experience, the given numbers seems to be at least one magnitude off, which seems like a lot, when I use the approx values for a Pro 6000 (96GB VRAM + 1792 GB/s)
I've been trying to get speech to text to work with a reasonable vocabulary on pis for a while. It's tough. All the modern models just need more GPU than is available
This is great, I've been trying to figure this stuff out recently.
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.
Is there a reliable guide somewhere to setting up local AI for coding (please don’t say ‘just Google it’ - that just results in a morass of AI slop/SEO pages with out of date, non-self-consistent, incorrect or impossible instructions).
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?
My Mac mini rocks qwen2.5 14b at a lightning fast 11/tokens a second. Which is actually good enough for the long term data processing I make it spend all day doing. It doesn’t lock up the machine or prevent its primary purpose as webserver from being fulfilled.
Does it make any sense? I tried few models at 128GB and it's all pretty much rubbish. Yes they do give coherent answers, sometimes they are even correct, but most of the time it is just plain wrong. I find it massive waste of time.
Hugging Face can already do this for you (with much more up-to-date list of available models). Also LM Studio. However they don't attempt to estimate tok/sec, so that's a cool feature. However I don't really trust those numbers that much because it is not incorporating information about the CPU, etc. True GPU offload isn't often possible on consumer PC hardware. Also there are different quants available that make a big difference.
if you do, would you still want to collect data in a single pane of glass? see my open source repo for aggregating harness data from multiple machine learning model harnesses & models into a single place to discover what you are working on & spending time & money. there is plans for a scrobble feature like last.fm but for agent research & code development & execution.
thanks, I'll check for comments, feel free to fork but if you want to contribute you'll have to find me off of github, I develop privately on my own self hosted gitlab server. good luck & God bless.
This seems to be estimating based on memory bandwidth / size of model, which is a really good estimate for dense models, but MoE models like GPT-OSS-20b don't involve the entire model for every token, so they can produce more tokens/second on the same hardware. 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.
(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.)
When running models on my phone - either through the web browser or via an app - is there any chance it uses the phone's NPU, or will these be GPU only?
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.
tbh i stopped caring about "can i run X locally" a while ago. for anything where quality matters (scripting, code, complex reasoning) the local models are just not there yet compared to API. where local shines is specific narrow tasks - TTS, embeddings, whisper for STT, stuff like that. trying to run a 70b model at 3 tok/s on your gaming GPU when you could just hit an API for like $0.002/req feels like a weird flex IMO
Is this just llmfit but a web version of it?
https://github.com/AlexsJones/llmfit
Yes. But llmfit is far more useful as it detects your system resources.
Honestly I was surprised about this. It accurately got my GPU and specs without asking for any permissions. I didnt realize I was exposing this info.
How could it not? That information is always available to userspace.
"Available to userspace" is a much different thing than "available to every website that wants it, even in private mode".
I too was a little surprised by this. My browser (Vivladi) makes a big deal about how privacy-conscious they are, but apparently browser fingerprinting is not on their radar.
It's pretty hard to avoid GPU fingerprinting if you have webgl/webgpu enabled
We switched to talking about llmfit in this subthread, it runs as native code.
Do you mean the OPs website? Mine's way off.
> Estimates based on browser APIs. Actual specs may vary
This lacks a whole lot of mobile GPUs. It also does not understand that you can share CPU memory with the GPU, or perform various KV cache offloading strategies to work around memory limits.
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.
Having the rating of how well the model will run for you is cool. I miss to also have some rating of the model capabilities (even if this is tricky). There are way too many to choose. And just looking at the parameter number or the used memory is not always a good indication of actual performance.
Cool thing!
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!
The RAM/VRAM cutoff matters more than the parameter count alone. A 13B model in Q4_K_M quantization fits in 8GB VRAM with reasonable throughput, but the same model in fp16 needs 26GB. Most calculators treat quantization as a footnote when it is actually the primary variable. The question is not "can I run 13B" but "what quantization level gives acceptable quality at my hardware ceiling".
i think the perplexity is more important than tokens per second. tokens per second is relatively useless in my opinion. there is nothing worse than getting bad results returned to you very quickly and confidently.
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.
No, getting bad results slowly is much worse. Bad results quickly and you can make adjustments.
But yes, if there is a choice I want quality over speed. At same quality, I definitely want speed.
This is awesome, it would be great to cross reference some intelligence benchmarks so that I can understand the trade off between RAM consumption, token rate and how good the model is
For some reason it doesn't react to changing the RAM amount in the combo box at the top. If I open this on my Ryzen AI Max 395+ with 32 GB of unified memory, it thinks nothing will fit because I've set it up to reserve 512MB of RAM for the GPU.
What is this S/A/B/C/etc. ranking? Is anyone else using it?
Just a tier list I think
Apparently S being a level above A comes from Japanese grading. I’ve been confused by that, too.
This feels a bit pessimistic. Qwen 3.5 35B-A3B runs at 38 t/s tg with llama.cpp (mmap enabled) on my Radeon 6800 XT.
At what quantization and with what size context window?
Why isn't there some kind of benchmark score in the list?
I don't see my beloved 5060ti. looks great though
This doesn't look accurate to me. I have an RX9070 and I've been messing around with Qwen 3.5 35B-A3B. According to this site I can't even run it, yet I'm getting 32tok/s ^.-
It seems to be missing a whole load of the quantized Qwen models, Qwen3.5:122b works fine in the 96GB GH200 (a machine that is also missing here....)
RTX Pro 6000 is a glaring omission.
Yeah, that's weird, seems it has later models, and earlier, but specifically not Pro 6000? Also, based on my experience, the given numbers seems to be at least one magnitude off, which seems like a lot, when I use the approx values for a Pro 6000 (96GB VRAM + 1792 GB/s)
No Nvidia Spark workstation is another omission.
Raspberry pi? Say 4B with 4GB of ram.
I also want to run vision like Yocto and basic LLM with TTS/STT
I've been trying to get speech to text to work with a reasonable vocabulary on pis for a while. It's tough. All the modern models just need more GPU than is available
Whispr?
For wakewords I have used pico rhino voice
I want to use these I2S breakout mics
It says "RAM - unknown", but doesn't give me an option to specify how much RAM I have. Why?
This is great, I've been trying to figure this stuff out recently.
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.
Ollama runs a web server that you use to interact with the models: https://docs.ollama.com/quickstart
You can also use the kubernetes operator to run them on a cluster: https://ollama-operator.ayaka.io/pages/en/
ssh?
Is there a reliable guide somewhere to setting up local AI for coding (please don’t say ‘just Google it’ - that just results in a morass of AI slop/SEO pages with out of date, non-self-consistent, incorrect or impossible instructions).
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.
Ollama or LM Studio are very simple to setup.
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?
Missing the A18 Neo! :)
For me the "can run" filter says "S/A/B" but lists S, A, B, and C and the "tight fit" filter says "C/D" but lists F.
Just FYI.
This just reminded me of this https://www.systemrequirementslab.com/cyri.
Not sure if it still works.
Cool website. The one that I'd really like to see there is the RTX 6000 Pro Blackwell 96 GB, though.
1. More title attributes please ("S 16 A 7 B 7 C 0 D 4 F 34", huh?)
2. Add a 150% size bonus to your site.
Otherwise, cool site, bookmarked.
My Mac mini rocks qwen2.5 14b at a lightning fast 11/tokens a second. Which is actually good enough for the long term data processing I make it spend all day doing. It doesn’t lock up the machine or prevent its primary purpose as webserver from being fulfilled.
Does it make any sense? I tried few models at 128GB and it's all pretty much rubbish. Yes they do give coherent answers, sometimes they are even correct, but most of the time it is just plain wrong. I find it massive waste of time.
I'm not sure how long ago you tried it, but look at Qwen 3.5 32b on a fast machine. Usually best to shut off thinking if you're not doing tool use.
Oh how cool. Always wanted to have a tool like this.
could you add raspi to the list to see which ridiculously small models it can run?
Hugging Face can already do this for you (with much more up-to-date list of available models). Also LM Studio. However they don't attempt to estimate tok/sec, so that's a cool feature. However I don't really trust those numbers that much because it is not incorporating information about the CPU, etc. True GPU offload isn't often possible on consumer PC hardware. Also there are different quants available that make a big difference.
On mobile it does not show the name of the model in favor of the other stats.
if you do, would you still want to collect data in a single pane of glass? see my open source repo for aggregating harness data from multiple machine learning model harnesses & models into a single place to discover what you are working on & spending time & money. there is plans for a scrobble feature like last.fm but for agent research & code development & execution.
https://github.com/russellballestrini/unfirehose-nextjs-logg...
thanks, I'll check for comments, feel free to fork but if you want to contribute you'll have to find me off of github, I develop privately on my own self hosted gitlab server. good luck & God bless.