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Show HN: Find the best local LLM for your hardware, ranked by benchmarks

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Re: Show HN: Find the best local LLM for your hardware, ranked by benchmarks

#12
Cool, but it looks like it doesn’t actually test anything on your machine? It does hardware detection and then some lookups. Maybe I missed it but I really want a tool like this to actually run a model on my machine to get the speed numbers.

I’ve been using RapidMLX for this. The integrated speed tests matter because the quality of the backend is a moving target and the quantization / MLX format conversion also matter. It’s not enough to say “oh use this model family with X parameters” you have to add the architecture specific quantization too.

https://github.com/raullenchai/Rapid-MLX

Re: Show HN: Find the best local LLM for your hardware, ranked by benchmarks

#13
This doesn't correclty detect the unified memory architecture for

GPU 0: STRXLGEN — 8.0 GB (ROCm 6.19.8-200.fc43.x86_64) — BW: N/A CPU: AMD RYZEN AI MAX+ 395 w/ Radeon 8060S — 16 cores (AVX2, AVX-512)

The 8GB is the reserved memory, but it's not the total available memory to the GPU.

Linux sets the unified memory like this on linux: https://www.jeffgeerling.com/blog/2025/increasing-vram-alloc...

Don't feel bad though, nvtop doesn't do it correctly either.

Re: Show HN: Find the best local LLM for your hardware, ranked by benchmarks

#14
post #3

Brew install is broken It seems pretty rubbish I have to say, its recommending me loads of qwen 2.5 which are really old and I'm easy running qwen3.5 and 3.6 models on this mac at decent quants

AI slop quality software for ya.

“I release software now, good luck everyone”

Re: Show HN: Find the best local LLM for your hardware, ranked by benchmarks

#15

This is very helpful too: https://www.canirun.ai/

Love that it defaults to the GPU being "NVIDIA GeForce 8800 GTX", a GPU released in 2006 with ~700MB of VRAM...

The estimates seems far off as well, took https://www.canirun.ai/model/gpt-oss-120b as an example, with a RTX Pro 6000 and every single number is off, and notably misses estimation for the most important quant for GPT-OSS, the MXFP4 variant.

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