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.)
lambda 18 minutes ago [-]
Yeah, I looked up some models I have actually run locally on my Strix Halo laptop, and its saying I should have much lower performance than I actually have on models I've tested.
For MoE models, it should be using the active parameters in memory bandwidth computation, not the total parameters.
pbronez 12 minutes ago [-]
The docs page addresses this:
> 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.
While your remark is valid, there's two small inaccuracies here:
> 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).
That's super handy, thanks for sharing the link. 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!
deanc 2 hours ago [-]
Yes. But llmfit is far more useful as it detects your system resources.
dgrin91 1 hours ago [-]
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.
spudlyo 16 minutes ago [-]
I run LibreWolf, which is configured to ask me before a site can use WebGL, which is commonly used for fingerprinting. I got the popup on this site, so I assume that's how they're doing it.
dekhn 1 hours ago [-]
How could it not? That information is always available to userspace.
bityard 26 minutes ago [-]
"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.
dekhn 20 minutes ago [-]
We switched to talking about llmfit in this subthread, it runs as native code.
swiftcoder 20 minutes ago [-]
It's pretty hard to avoid GPU fingerprinting if you have webgl/webgpu enabled
rithdmc 52 minutes ago [-]
Do you mean the OPs website? Mine's way off.
> Estimates based on browser APIs. Actual specs may vary
LeifCarrotson 1 hours ago [-]
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.
sxates 2 hours ago [-]
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!
carra 54 minutes ago [-]
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.
Felixbot 1 hours ago [-]
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".
freediddy 38 minutes ago [-]
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.
sroussey 34 minutes ago [-]
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.
amelius 19 minutes ago [-]
It would be great if something like this was built into ollama, so you could easily list available models based on your current hardware setup, from the CLI.
sdingi 21 minutes ago [-]
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.
phelm 2 hours ago [-]
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
am17an 18 minutes ago [-]
You can still run larger MoE models using expert weight off-loading to the CPU for token generation. They are by and large useable, I get ~50 toks/second on a kimi linear 48B (3B active) model on a potato PC + a 3090
orthoxerox 43 minutes ago [-]
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.
bityard 17 minutes ago [-]
Yeah, this site is iffy at best. I didn't even see Strix Halo on the list, but I selected 128GB and bumped up the memory bandwidth. It says gpt-oss-120b "barely runs" at ~2 t/s.
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.
tcbrah 22 minutes ago [-]
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
AstroBen 52 minutes ago [-]
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 ^.-
misnome 37 minutes ago [-]
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....)
GrayShade 2 hours ago [-]
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.
Aurornis 49 minutes ago [-]
At what quantization and with what size context window?
sshagent 1 hours ago [-]
I don't see my beloved 5060ti. looks great though
John23832 2 hours ago [-]
RTX Pro 6000 is a glaring omission.
embedding-shape 2 hours ago [-]
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)
schaefer 2 hours ago [-]
No Nvidia Spark workstation is another omission.
amelius 30 minutes ago [-]
What is this S/A/B/C/etc. ranking? Is anyone else using it?
relaxing 25 minutes ago [-]
Apparently S being a level above A comes from Japanese grading. I’ve been confused by that, too.
swiftcoder 17 minutes ago [-]
It's very common in Japanese-developed video games as well
vikramkr 29 minutes ago [-]
Just a tier list I think
ge96 1 hours ago [-]
Raspberry pi? Say 4B with 4GB of ram.
I also want to run vision like Yocto and basic LLM with TTS/STT
boutell 38 minutes ago [-]
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
I haven't tried on a raspberry pi, but on Intel it uses a little less than 1s of CPU time per second of audio. Using https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/a... for chunked streaming inference, it takes 6 cores to process audio ~5x faster than realtime. I expect with all cores on a Pi 4 or 5, you'd probably be able to at least keep up with realtime.
(Batch inference, where you give it the whole audio file up front, is slightly more efficient, since chunked streaming inference is basically running batch inference on overlapping windows of audio.)
EDIT: there are also the multitalker-parakeet-streaming-0.6b-v1 and nemotron-speech-streaming-en-0.6b models, which have similar resource requirements but are built for true streaming inference instead of chunked inference. In my tests, these are slightly less accurate. In particular, they seem to completely omit any sentence at the beginning or end of a stream that was partially cut off.
ge96 33 minutes ago [-]
Whispr?
For wakewords I have used pico rhino voice
I want to use these I2S breakout mics
vova_hn2 2 hours ago [-]
It says "RAM - unknown", but doesn't give me an option to specify how much RAM I have. Why?
amelius 31 minutes ago [-]
Why isn't there some kind of benchmark score in the list?
mrdependable 2 hours ago [-]
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.
If anyone hasn't tried Qwen3.5 on Apple Silicon, I highly suggest you to! Claude level performance on local hardware. If the Qwen team didn't get fired, I would be bullish on Local LLM.
jrmg 53 minutes ago [-]
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.
AstroBen 43 minutes ago [-]
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?
jrmg 12 minutes ago [-]
Right - setting up LM studio is not hard. But how do I connect LM Studio to Copilot, or set up an agent?
brcmthrowaway 7 minutes ago [-]
Basically LM Studio has a server that serves models over HTTP (localhost). Configure/enable the server and connect OpenCode to it.
Cool website. The one that I'd really like to see there is the RTX 6000 Pro Blackwell 96 GB, though.
S4phyre 2 hours ago [-]
Oh how cool. Always wanted to have a tool like this.
kylehotchkiss 39 minutes ago [-]
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.
g_br_l 2 hours ago [-]
could you add raspi to the list to see which ridiculously small models it can run?
nilslindemann 33 minutes ago [-]
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.
metalliqaz 1 hours ago [-]
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.
charcircuit 2 hours ago [-]
On mobile it does not show the name of the model in favor of the other stats.
varispeed 46 minutes ago [-]
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.
boutell 37 minutes ago [-]
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.
unfirehose 51 minutes ago [-]
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.
aplomb1026 31 minutes ago [-]
[dead]
uncSoft 1 hours ago [-]
[dead]
Rendered at 18:02:29 GMT+0000 (Coordinated Universal Time) with Vercel.
(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.
https://www.canirun.ai/docs
> 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).
https://github.com/AlexsJones/llmfit
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.
> Estimates based on browser APIs. Actual specs may vary
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.
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!
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.
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.
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.
I also want to run vision like Yocto and basic LLM with TTS/STT
I haven't tried on a raspberry pi, but on Intel it uses a little less than 1s of CPU time per second of audio. Using https://github.com/NVIDIA-NeMo/NeMo/blob/main/examples/asr/a... for chunked streaming inference, it takes 6 cores to process audio ~5x faster than realtime. I expect with all cores on a Pi 4 or 5, you'd probably be able to at least keep up with realtime.
(Batch inference, where you give it the whole audio file up front, is slightly more efficient, since chunked streaming inference is basically running batch inference on overlapping windows of audio.)
EDIT: there are also the multitalker-parakeet-streaming-0.6b-v1 and nemotron-speech-streaming-en-0.6b models, which have similar resource requirements but are built for true streaming inference instead of chunked inference. In my tests, these are slightly less accurate. In particular, they seem to completely omit any sentence at the beginning or end of a stream that was partially cut off.
For wakewords I have used pico rhino voice
I want to use these I2S breakout mics
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’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?
Try this article https://advanced-stack.com/fields-notes/qwen35-opencode-lm-s...
I'm looking for an alternative to OpenCode though, I can barely see the UI.
Just FYI.
Not sure if it still works.
2. Add a 150% size bonus to your site.
Otherwise, cool site, bookmarked.
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.