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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly.
| Date | Stars |
|---|---|
| 2026-07-31 | 6068 |
| 2026-08-02 | 6068 |
| 2026-08-04 | 6124 |
| 2026-08-06 | 6124 |
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# whichllm [](https://pypi.org/project/whichllm/) [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://github.com/Andyyyy64/whichllm/actions/workflows/test.yml) [](https://github.com/sponsors/Andyyyy64) <p align="center"> <a href="https://trendshift.io/repositories/30336" target="_blank"><img src="https://trendshift.io/api/badge/repositories/30336" alt="Andyyyy64%2Fwhichllm | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p> **Find the best local LLM that actually runs on your hardware.** Auto-detects your GPU/CPU/RAM and ranks the top models from HuggingFace that fit your system. [日本語版はこちら](docs/README.ja.md) ## Quick start Run the recommendation command once, with no project setup. ```bash uvx whichllm@latest ``` Simulate a GPU before you buy hardware. ```bash uvx whichllm@latest --gpu "RTX 4090" ``` Install it when you use it often. ```bash uv tool install whichllm uv tool upgrade whichllm # update an existing install ``` Other install paths. ```bash brew install andyyyy64/whichllm/whichllm pip install whichllm ``` ## Want a safer pick? By default, whichllm is ambitious. It ranks the best model that looks runnable on your machine, including partial RAM offload and near-edge VRAM fits when they seem usable. If you want a more comfortable LM Studio-style recommendation, start with: ```bash uvx whichllm@latest --gpu-only --speed usable --vram-headroom 1GB ``` This keeps only models that fit fully in GPU VRAM, filters out slow estimates, and leaves extra VRAM for runtime overhead. If LM Studio still says the model is slightly too large, increase the headroom: ```bash uvx whichllm@latest --gpu-only --speed usable --vram-headroom 1.5GB ``` ## Common workflows After install, run `whichllm` directly. For one-off runs, replace `whichllm` with `uvx whichllm@latest`. ```bash # Best models for this machine whichllm # Pretend you have a specific GPU whichllm --gpu "RTX 4090" # Override detected iGPU/unified-memory limits whichllm --vram 8 --ram-bandwidth 68 # Only show models that fit fully in GPU VRAM whichllm --gpu-only whichllm --fit gpu # Simulate a multi-GPU workstation whichllm --gpu "2x RTX 4090" # Hide models that are technically runnable but too slow whichllm --speed usable whichllm --speed fast # Pasteable GitHub / Slack / Discord output whichllm --markdown # Compare upgrade candidates whichllm upgrade "RTX 4090" "RTX 5090" "H100" # Find the GPU needed for a model whichllm plan "llama 3 70b" # Start a chat with a model whichllm run "qwen 2.5 1.5b gguf" # Print copy-paste Python whichllm snippet "qwen 7b" # Return JSON for scripts whichllm --top 1 --json ```  ## See it ```text $ whichllm --gpu "RTX 4090" #1 Qwen/Qwen3.6-27B 27.8B Q5_K_M score 92.8 27 t/s #2 Qwen/Qwen3-32B 32.0B Q4_K_M score 83.0 31 t/s #3 Qwen/Qwen3-30B-A3B 30.0B Q5_K_M score 82.7 102 t/s ``` The 32B model **fits your card fine** — whichllm still ranks the 27B #1, because it scores higher on real benchmarks and is a newer generation. A size-only "what fits?" tool would hand you the bigger one. That gap is the whole point of whichllm. (Note #3: a MoE model at 102 t/s — speed is ranked on *active* params, quality on *total*.) ## What can I run? Real top picks (snapshot 2026-05 — your results track **live** HuggingFace data, this is not a static list): | Hardware | VRAM | Top pick | Speed | |---|---|---|---| | RTX 5090 | 32 GB | `Qwen3.6-27B` · Q6_K · score 94.7 | ~40 t/s | | RTX 4090 / 3090 | 24 GB | `Qwen3.6-27B` · Q5_K_M · score 92.8
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7445fbcc4bff3da9, topic:inference, desc:local llm
matched fp:7445fbcc4bff3da9, topic:gpu
matched fp:7445fbcc4bff3da9, topic:llm