GitHub - Andyyyy64/whichllm: 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.

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

See it $ 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 ~27 t/s

RTX 4060 8 GB Qwen3-14B · Q3_K_M · score 71.0 ~22 t/s

Apple M3 Max 36 GB Qwen3.6-27B · Q5_K_M · score 89.4 ~9 t/s

CPU only — gpt-oss-20b (MoE) · Q4_K_M · score 45.2 ~6 t/s

whichllm --gpu "<your card>" to simulate any of these before you buy.

Useful? A GitHub star helps other people find it — and I'd genuinely like to know what it picked for your rig: drop it in Issues.

Why whichllm? Fitting a model into your VRAM is the easy part. The hard part is knowing which of the models that fit is actually the best — and that is what whichllm is built to get right.

Evidence-based ranking, not a size heuristic — The top pick is chosen from merged real benchmarks (LiveBench, Artificial Analysis, Aider, multimodal/vision, Chatbot Arena ELO, Open LLM Leaderboard) — never "the biggest model that happens to fit." Recency-aware — Stale leaderboards are demoted along each model's lineage, so a 2024 model can't outrank a current-generation one on an outdated score. The benchmark snapshot date is printed under every ranking, so a stale recommendation is self-evident instead of silently trusted. Evidence-graded and guarded — Every score is tagged direct / variant / base / interpolated / self-reported and discounted by confidence. Fabricated uploader claims and cross-family inheritance (a small fork borrowing its much larger base's score) are actively rejected. Architecture-aware estimates — VRAM = weights + GQA KV cache + activation + overhead; speed is bandwidth-bound with per-quant efficiency, per-backend factors, MoE active-vs-total split, and unified-memory vs discrete-PCIe partial-offload modeling. One command, scriptable — whichllm prints the answer; add --json | jq for pipelines.

No TUI, no keybindings to memorize. Live data — Models fetched directly from the HuggingFace API, with curated frozen fallbacks for offline or rate-limited use.

Features

Auto-detect hardware — NVIDIA, AMD, Apple Silicon, CPU-only Smart ranking — Scores models by VRAM fit, speed, and benchmark quality One-command chat — whichllm run downloads and starts a chat session instantly Code snippets — whichllm snippet prints ready-to-run Python for any model Live data — Fetches models directly from HuggingFace (cached for performance) Benchmark-aware — Integrates real eval scores with confidence-based dampening Task profiles — Filter by general, coding, vision, or math use cases GPU simulation — Test with any GPU: whichllm --gpu "RTX 4090" Hardware planning — Reverse lookup: whichllm plan "llama 3 70b" JSON output — Pipe-friendly: whichllm --json

Run & Snippet Try any model with a single command. No manual installs needed — whichllm creates an isolated environment via uv, installs dependencies, downloads the model, and starts an interactive chat.

# Chat with a model (auto-picks the best GGUF variant) whichllm run "qwen 2.5 1.5b gguf"

# Auto-pick the best model for your hardware and chat whichllm run

# CPU-only mode whichllm run "phi 3 mini gguf" --cpu-only Works with all model formats:

GGUF — via llama-cpp-python (lightweight, fast) AWQ / GPTQ — via transformers + autoawq / auto-gptq FP16 / BF16 — via transformers

Get a copy-paste Python snippet instead: whichllm snippet "qwen 7b" from llama_cpp import Llama

llm = Llama.from_pretrained( repo_id="Qwen/Qwen2.5-7B-Instruct-GGUF", filename="qwen2.5-7b-instruct-q4_k_m.gguf", n_ctx=4096, n_gpu_layers=-1, verbose=False, )

output = llm.create_chat_completion( messages=[{"role": "user", "content": "Hello!"}], ) print(output["choices"][0]["message"]["content"]) Install pipx (recommended) pipx install whichllm Homebrew brew tap Andyyyy64/whichllm brew install whichllm pip pip install whichllm Development git clone https://github.com/Andyyyy64/whichllm.git cd whichllm uv sync --dev uv run whichllm uv run pytest Usage # Auto-detect hardware and show best models whichllm

# Simulate a GPU (e.g. planning a purchase) whichllm --gpu "RTX 4090" whichllm --gpu "RTX 5090"

# CPU-only mode whichllm --cpu-only

# More results / filters whichllm --top 20 whichllm --quant Q4_K_M whichllm --min-speed 30 whichllm --evidence base # allow id/base-model matches whichllm --evidence strict # id-exact only (same as --direct) whichllm --direct

# JSON output whichllm --json

# Force refresh (ignore cache) whichllm --refresh

# Show hardware info only whichllm hardware

# Plan: what GPU do I need for a specific model?

whichllm plan "llama 3 70b" whichllm plan "Qwen2.5-72B" --quant Q8_0 whichllm plan "mistral 7b" --context-length 32768

# Run: download and chat with a model instantly whichllm run "qwen 2.5 1.5b gguf" whichllm run # auto-pick best for your hardware

# Snippet: print ready-to-run Python code whichllm snippet "qwen 7b" whichllm snippet "llama 3 8b gguf" --quant Q5_K_M Integrations Ollama Find the best model and run it directly: # Pick the top model and run it with Ollama whichllm --top 1 --json | jq -r '.models[0].model_id' | xargs ollama run

# Find the best coding model whichllm --profile coding --top 1 --json | jq -r '.models[0].model_id' | xargs ollama run Shell alias Add to your .bashrc / .zshrc: alias bestllm='whichllm --top 1 --json | jq -r ".models[0].model_id"' # Usage: ollama run $(bestllm) Scoring Each model gets a 0-100 score. Benchmark quality and size form the core; evidence confidence and runtime fit then scale it, with speed, source trust, and popularity as adjustments.

Factor Effect Description

Benchmark quality core Merged LiveBench / Artificial Analysis / Aider / Vision / Arena ELO / Open LLM Leaderboard, weighted by source confidence

Model size up to 35 log2-scaled world-knowledge proxy (MoE uses total params)

Quantization × penalty Lower-bit quants discounted multiplicatively

Evidence confidence ×0.55–1.0 none / self-reported ×0.55, inherited

×0.78, direct full

Runtime fit ×0.50–1.0 partial-offload ×0.72, CPU-only ×0.50

Speed -8 to +8 Usability gate vs a fit-dependent tok/s floor

Source trust -5 to +5 Official-org bonus, known-repackager penalty

Popularity tie-breaker Downloads/likes; weight shrinks as evidence strengthens

Score markers:

~ (yellow) — No direct benchmark; score inherited/interpolated from the model family ? (yellow) — No benchmark data available

How it works Data pipeline

Model fetching — Fetches popular models from HuggingFace API:

Text-generation (downloads + recently updated) GGUF-filtered (separate query for coverage) Vision models (image-text-to-text) when --profile vision or any

Benchmark sources — Current tier (LiveBench, Artificial Analysis Index, Aider) merged live when reachable, plus a curated multimodal / vision index; frozen tier (Open LLM Leaderboard v2, Chatbot Arena ELO). Tiers have separate caps and lineage-aware recency demotion so stale leaderboards stop over-rewarding older generations.

Benchmark evidence — Five resolution levels, increasingly discounted:

direct — Exact model ID match variant — Suffix-stripped or -Instruct variant base_model — Base model from cardData line_interp — Size-aware interpolation within model family self_reported — Uploader-claimed eval (heavily discounted)

Inheritance is rejected when a model's params diverge more than 2× from its family's dominant member, catching draft / MTP / abliterated forks that share a family_id with a much larger base.

Cache — ~/.cache/whichllm/:

models.json — 6h TTL benchmark.json — 24h TTL

Ranking engine

Hardware detection — NVIDIA (nvidia-ml-py), AMD (dbgpu/ROCm), Apple Silicon (Metal), CPU cores, RAM, disk VRAM estimation — Weights + KV cache + activation + framework overhead (~500MB) Compatibility — Full GPU / Partial Offload / CPU-only; compute capability and OS checks Speed — tok/s from GPU memory bandwidth lookup (constants.py) Scoring — Benchmark (with confidence dampening), size, quantization penalty, fit type, speed, popularity, source trust (official vs repackager) Backend filter — Apple Silicon and CPU-only restrict to GGUF for stability; Linux+NVIDIA allows AWQ/GPTQ

Project structure src/whichllm/ ├── cli.py # Typer CLI: main, plan, run, snippet, hardware ├── constants.py # GPU bandwidth, quantization bytes, compute capability ├── hardware/ │ ├── detector.py # Orchestrates GPU/CPU/RAM detection │ ├── nvidia.py # NVIDIA GPU via nvidia-ml-py │ ├── amd.py # AMD GPU (Linux) │ ├── apple.py # Apple Silicon (Metal) │ ├── cpu.py # CPU name, cores, AVX support │ ├── memory.py # RAM and disk free │ ├── gpu_simulator.py # --gpu flag: synthetic GPU from name │ └── types.py # GPUInfo, HardwareInfo ├── models/ │ ├── fetcher.py # HuggingFace API, model parsing, evalResults │ ├── benchmark.py # Arena ELO, Leaderboard (parquet/rows API) │ ├── grouper.py # Family grouping by base_model and name │ ├── cache.py # JSON cache with TTL │ └── types.py # ModelInfo, GGUFVariant,

ModelFamily ├── engine/ │ ├── vram.py # VRAM = weights + KV cache + activation + overhead │ ├── compatibility.py# Fit type, disk check, compute/OS warnings │ ├── performance.py # tok/s from bandwidth │ ├── quantization.py # Bytes per weight, quality penalty, non-GGUF inference │ ├── ranker.py # Scoring, evidence filter, profile/match │ └── types.py # CompatibilityResult └── output/ └── display.py # Rich table, JSON output, hardware/plan displays

Contributing Contributions are welcome! See CONTRIBUTING.md for guidelines. Requirements

Python 3.11+ NVIDIA GPU detection via nvidia-ml-py (included by default) AMD / Apple Silicon detected automatically

License MIT