{"repo":"Andyyyy64/whichllm","free":true,"listed":false,"github":"https://github.com/Andyyyy64/whichllm","clone":"git clone https://github.com/Andyyyy64/whichllm.git","description":"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.","language":"Python","stars":6327,"topics":["ai","cli","llm","local-llm","command-line-tool","gguf","gpu","huggingface","inference","ollama"],"license":"MIT","category":"cli-tools","readme_excerpt":"whichllm 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 Quick start Run the recommendation command once, with no project setup. Simulate a GPU before you buy hardware. Install it when you use it often. Other install paths. 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: 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: Common workflows After install, run whichllm directly. For one-off runs, replace whichllm with uvx whichllm@latest . See it 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","default_branch":null,"files":null,"tree":[],"storefront":"/r/Andyyyy64","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Andyyyy64/whichllm/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}