LLM Scan

A fast CLI that scans your hardware and tells you which local LLMs will actually run on your machine.

Features

• Auto-detects your hardware -OS, CPU, RAM, and GPU memory in seconds
• Multi-vendor GPU support -NVIDIA (nvidia-smi), AMD ROCm (rocm-smi), Intel Arc (xpu-smi/clinfo), Apple Silicon (unified memory), Windows (Get-CimInstance/wmic)
• Smart fitness scoring -Rates every model as great, ok, tight, or no based on your VRAM, RAM, and multi-GPU setup
• 45+ bundled models -Llama, Qwen, Mistral, Gemma, Phi, DeepSeek, CodeLlama, StarCoder, and more
• Hugging Face search -Find GGUF models on Hugging Face and add them to your local catalog
• Auto-computed VRAM -Adds models with formula-derived memory requirements from parameter count and quantization
• Multi-GPU aware -Accounts for tensor parallelism across multiple GPUs
• CPU-only inference -Recognizes when you have enough RAM to run models without a GPU
• Beautiful terminal UI -Rich tables, color-coded ratings, ASCII banner
• Custom catalogs -Bring your own model list as a JSON file
• JSON output -Pipe results into scripts or dashboards

Install

# With pip
pip install llmscan

# With pipx (isolated install)
pipx install llmscan

# With uv
uv pip install llmscan

# From source
git clone https://github.com/adityaarakeri/llmscan.git
cd llmscan
pip install -e .

The installed command remains llmscan.

Quick Start

# Show banner + hardware summary + compatible models
llmscan

# Check version
llmscan --version

Usage

scan -Inspect your hardware

llmscan scan              # Rich formatted hardware details
llmscan scan --json       # Machine-readable JSON output

list -List compatible models

llmscan list                          # Show models rated "tight" and above
llmscan list --min-rating great       # Only show "great" fits
llmscan list --min-rating no          # Show all models including non-fits
llmscan list --json                   # JSON output with machine profile + models
llmscan list --catalog my_models.json # Use a custom catalog file

explain -Deep-dive on a specific model

llmscan explain llama-3.1-8b-instruct              # Why does this model fit?
llmscan explain qwen2.5-72b-instruct               # Why doesn't it?
llmscan explain my-model --catalog my_models.json   # Explain from a custom catalog

search -Find GGUF models on Hugging Face

llmscan search llama              # Search for Llama GGUF models
llmscan search "codellama 13b"    # More specific search
llmscan search mistral --limit 5  # Limit results
llmscan search qwen --json        # JSON output

add -Add a model to your local catalog

VRAM/RAM requirements are auto-computed from the parameter count and quantization type.

# Add by specifying params and quant manually
llmscan add my-model --params-b 7 --quant Q4_K_M --family Llama
llmscan add my-model --params-b 7 --quant Q4_K_M --family Llama --notes "My fine-tune"

# Add from a Hugging Face repo (auto-detects params and quant)
llmscan add TheBloke/Llama-2-7B-GGUF

# Overwrite an existing entry
llmscan add my-model --params-b 7 --quant Q8_0 --family Llama --force

# JSON output
llmscan add my-model --params-b 7 --quant Q4_K_M --json

Supported quantization types: Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, Q4_0, Q4_K_S, Q4_K_M, Q5_0, Q5_K_S, Q5_K_M, Q6_K, Q8_0, F16, IQ2_XS, IQ3_XS

Models are saved to ~/.llmscan/catalog.json and automatically merged with the bundled catalog.

remove -Remove a model from your local catalog

llmscan remove my-model   # Only removes user-added models, not bundled ones

Example Output

llmscan list --min-rating ok

┃ Model                        ┃ Family  ┃ Params ┃ Fit   ┃ Min VRAM ┃ Rec VRAM ┃ Rec RAM ┃
┃ llama-3.1-8b-instruct        ┃ Llama   ┃ 8B     ┃ great ┃ 5.0 GB   ┃ 6.2 GB   ┃ 10 GB   ┃
┃ mistral-7b-instruct-v0.3     ┃ Mistral ┃ 7B     ┃ great ┃ 4.4 GB   ┃ 5.5 GB   ┃ 8 GB    ┃
┃ phi-4-mini                   ┃ Phi     ┃ 4B     ┃ great ┃ 2.5 GB   ┃ 3.1 GB   ┃ 6 GB    ┃
...

Rating System

Rating	Meaning
great	GPU VRAM and RAM both meet recommended targets -best experience
ok	Meets minimum requirements, may need moderate context limits or uses CPU-only inference
tight	Runs with CPU offload, reduced context, or multi-GPU splitting -expect slower performance
no	Hardware is below practical minimums

Supported Hardware

Vendor	Detection Method	Notes
NVIDIA	nvidia-smi	Discrete GPUs with CUDA
AMD	rocm-smi	GPUs with ROCm drivers
Intel	xpu-smi, clinfo	Arc and Data Center GPUs
Apple	sysctl	Apple Silicon unified memory (65% GPU estimate)
Windows	Get-CimInstance, wmic	Fallback for any GPU on Windows

Custom Catalogs

Create a JSON file with your own model entries:

[
  {
    "id": "my-custom-model-7b",
    "family": "Custom",
    "params_b": 7,
    "quant": "Q4_K_M",
    "min_vram_gb": 5,
    "recommended_vram_gb": 8,
    "recommended_ram_gb": 16,
    "notes": "My fine-tuned model"
  }
]

Then pass it with --catalog:

llmscan list --catalog my_models.json
llmscan explain my-custom-model-7b --catalog my_models.json

Required Fields

Field	Type	Description
id	string	Unique model identifier
family	string	Model family name (e.g., "LLaMA", "Mistral")
params_b	number	Parameter count in billions
quant	string	Quantization format (e.g., "Q4_K_M", "Q5_K_M")
min_vram_gb	number	Minimum GPU VRAM in GB
recommended_vram_gb	number	Recommended GPU VRAM in GB
recommended_ram_gb	number	Recommended system RAM in GB
notes	string	Additional notes about the model

Development

git clone https://github.com/adityaarakeri/llmscan.git
cd llmscan

# Install with dev dependencies
uv pip install -e ".[test,dev]"

# Run tests
uv run pytest

# Lint and format
uv run ruff check .
uv run ruff format .

# Type check
uv run mypy llmscan/

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/my-feature)
3. Make your changes and add tests
4. Ensure all checks pass: uv run pytest && uv run ruff check . && uv run mypy llmscan/
5. Open a pull request

License

MIT -see LICENSE for details.