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whichllm

PyPI version Python 3.11+ License: MIT Tests Sponsor

Andyyyy64%2Fwhichllm | Trendshift

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.

uvx whichllm@latest

Simulate a GPU before you buy hardware.

uvx whichllm@latest --gpu "RTX 4090"

Install it when you use it often.

uv tool install whichllm
uv tool upgrade whichllm  # update an existing install

Other install paths.

brew install andyyyy64/whichllm/whichllm
pip install whichllm

Common workflows

After install, run whichllm directly. For one-off runs, replace whichllm with uvx whichllm@latest.

# Best models for this machine
whichllm

# Pretend you have a specific GPU
whichllm --gpu "RTX 4090"

# 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

demo

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>" simulates any of these before you buy. By default, rankings include full-GPU, partial-offload, and CPU-only candidates when they are usable. Use --gpu-only or --fit full-gpu when you only want models that fit entirely in GPU VRAM. The default table shows memory, estimated generation speed, fit type, and published date. Speed is colored by practical usability: under 4 tok/s is red, 4-10 is yellow, 10-30 is green, and 30+ is bright green. ~ / ? still mark estimate confidence.

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, Intel, 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"
  • Multi-GPU simulation — Repeat --gpu, use commas, or write 2x RTX 4090
  • Full-GPU filter — --gpu-only / --fit full-gpu hides offload candidates
  • Speed-aware filtering — --speed usable|fast hides slow rows by threshold
  • Markdown output — --markdown / -m prints pasteable GFM tables
  • Runtime memory budgets — --vram-headroom and --ram-budget avoid edge fits
  • Hardware planning — Reverse lookup: whichllm plan "llama 3 70b"
  • Upgrade planning — Compare your current machine with candidate GPUs
  • 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.

run demo

# 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"])

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"
# Specify variant
whichllm --gpu "RTX 5060 16"
# Simulate multiple GPUs
whichllm --gpu "2x RTX 4090"
whichllm --gpu "RTX 4090" --gpu "RTX 3090"
whichllm --gpu "RTX 4090, RTX 3090"

# Only show models that fit entirely in GPU VRAM
whichllm --gpu-only
whichllm --fit gpu
whichllm --fit full-gpu

# Avoid edge fits and background-RAM surprises
whichllm --vram-headroom 1.5GB
whichllm --ram-budget available
whichllm --ram-budget 8GB

# CPU-only mode
whichllm --cpu-only

# More results / filters
whichllm --top 20
whichllm --details          # show Downloads metadata instead of runtime columns
whichllm --speed usable     # minimum 10 tok/s
whichllm --speed fast       # minimum 30 tok/s
whichllm --min-speed 4      # exact tok/s floor
whichllm --markdown         # pasteable GitHub-Flavored Markdown table
whichllm --profile coding
whichllm --context-length 64k
whichllm --quant Q4_K_M
whichllm --min-speed 30     # exact tok/s floor
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

# Upgrade: compare your current machine against candidate GPUs
whichllm upgrade "RTX 4090" "RTX 5090" "H100"
whichllm upgrade "Apple M4 Max" --top 5

# 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

Markdown output is intended for GitHub issues, READMEs, Slack, Discord, and blog posts:

whichllm --markdown
whichllm -m --top 5 --gpu "RTX 4090"

JSON model rows include fit_type, vram_required_bytes, vram_available_bytes, uses_multi_gpu, multi_gpu_effective_vram_bytes, estimated_tok_per_sec, speed_confidence, speed_range_tok_per_sec, speed_notes, benchmark_source, and benchmark_confidence. The speed range is a planning range, not a live benchmark.

Integrations

Ollama

Use JSON output to feed scripts that map HuggingFace IDs to your local Ollama model names:

# Pick the top HuggingFace model ID
whichllm --top 1 --json | jq -r '.models[0].model_id'

# Find the best coding model ID
whichllm --profile coding --top 1 --json | jq -r '.models[0].model_id'

Ollama model names do not always match HuggingFace repo IDs, so a small mapping step is usually needed before 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; reported with confidence and range metadata
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
  • !sr (bright yellow) — Uploader-reported benchmark only, not independently verified
  • ? (red) — No benchmark data available

Speed display:

  • red — Slow generation speed (<4 tok/s)
  • yellow — Marginal generation speed (4-10 tok/s)
  • green — Usable generation speed (10-30 tok/s)
  • bright green — Fast local generation speed (>=30 tok/s)
  • ~ (yellow) — Estimated tok/s range is available
  • ? (red) — Low-confidence speed estimate; backend/runtime sensitivity is high

Documentation

How it works

Data pipeline

  1. 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

  2. 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.

  3. 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.

  4. Cache — normally ~/.cache/whichllm/, or $XDG_CACHE_HOME/whichllm/ when XDG_CACHE_HOME is set to an absolute path:

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

Ranking engine

  1. Hardware detection — NVIDIA (nvidia-ml-py), AMD (ROCm/dbgpu), Intel, Apple Silicon (Metal), CPU cores, RAM, disk
  2. VRAM estimation — Weights + KV cache + activation + framework overhead (~500MB)
  3. Compatibility — Full GPU / Partial Offload / CPU-only; compute capability and OS checks
  4. Speed — tok/s from GPU memory bandwidth, quantization, backend, fit type, and MoE active parameters
  5. Scoring — Benchmark (with confidence dampening), size, quantization penalty, fit type, speed, popularity, source trust (official vs repackager)
  6. 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        # Backward-compatible exports for registry data
├── data/               # GPU, quantization, framework, and lineage registries
├── 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/
    ├── ranking.py      # Rich hardware and recommendation tables
    ├── json_output.py  # Ranking, plan, and upgrade JSON
    ├── plan.py         # plan command display
    ├── upgrade.py      # upgrade comparison display
    └── display.py      # Compatibility re-export shim

Development

git clone https://github.com/Andyyyy64/whichllm.git
cd whichllm
uv sync --dev
uv run whichllm
uv run pytest

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Support

If whichllm helped you find a model or avoid a bad hardware guess, sponsoring is appreciated. It helps keep the project maintained: hardware reports, packaging, test fixtures, benchmark updates, and support for more machines.

whichllm will stay open-source either way. Issues and PRs are always welcome.

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.

Star History

Star History Chart

Requirements

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

License

MIT

About

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.

Topics

python cli ai gpu inference benchmarks command-line-tool vram huggingface apple-silicon llm local-llm ollama gguf

Resources

Readme

License

MIT license

Code of conduct

Code of conduct

Contributing

Contributing
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Releases 13

v0.5.12 Latest
Jun 18, 2026
+ 12 releases

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