ollama-scout 0.1.1

Scan your hardware and find compatible Ollama LLMs

ollama-scout

Scan your hardware. Find the right LLMs. Pull them instantly.

ollama-scout is a cross-platform CLI tool that detects your GPU VRAM, CPU, and RAM, then recommends compatible Ollama models grouped by use case.

Demo

╭──────────────────────────────────────────────────────────╮
│  ollama-scout  |  LLM Hardware Advisor                   │
╰──────────────────────────────────────────────────────────╯

                   System Hardware
╭─────────────────┬────────────────────────────────────────╮
│ OS              │ Linux                                  │
│ CPU             │ AMD Ryzen 9 5900X                      │
│ Cores / Threads │ 12 cores / 24 threads                  │
│ RAM             │ 32.0 GB                                │
│ GPU             │ NVIDIA RTX 3080 (10.0 GB VRAM)         │
╰─────────────────┴────────────────────────────────────────╯

  Coding Models
  Model             Tag      Quant    Size    Fit        Mode     Status
  deepseek-coder    6.7b     Q4_K_M   3.8GB   Excellent  GPU      Available
  codellama         7b       Q4_K_M   3.8GB   Excellent  GPU      Pulled
  qwen2.5-coder    7b       Q4_K_M   4.4GB   Excellent  GPU      Available

  Reasoning Models
  deepseek-r1       7b       Q4_K_M   4.7GB   Excellent  GPU      Available
  phi4              14b      Q4_K_M   8.4GB   Good       CPU+GPU  Available

  Chat Models
  llama3.2          3b       Q4_K_M   2.0GB   Excellent  GPU      Pulled
  mistral           7b       Q4_K_M   4.1GB   Excellent  GPU      Available

Interactive Mode

Just run ollama-scout with no arguments for a guided experience:

ollama-scout

The interactive mode walks you through hardware scanning, use case selection, and model recommendations step by step — no flags needed. You can also launch it explicitly with ollama-scout -i.

How It Works

1. Scan       Detects GPU VRAM, CPU cores/threads, RAM
              Supports NVIDIA, AMD (ROCm), Apple Silicon unified memory
                                    |
2. Fetch      Pulls latest models from Ollama library API
              Falls back to built-in list if offline
                                    |
3. Score      Matches each model variant to your hardware
              GPU fit > CPU+GPU offload > CPU-only > excluded
                                    |
4. Recommend  Groups results by use case: Coding, Reasoning, Chat
              Shows fit label, run mode, and pull status

Installation

git clone https://github.com/sandy-sp/ollama-scout.git
cd ollama-scout
pip install -r requirements.txt

Requires Python 3.10+ and Ollama installed.

Usage

ollama-scout                                # Interactive guided mode (default)
ollama-scout -i                             # Explicit interactive mode
python main.py                              # Full scan, grouped by use case
python main.py --use-case coding            # Filter by use case
python main.py --flat                       # Flat list instead of grouped
python main.py --top 20                     # Show top 20 results
python main.py --offline                    # Use built-in model list (no network)
python main.py --benchmark                  # Show inference speed estimates
python main.py --model deepseek-coder       # Detail view for a specific model
python main.py --export                     # Auto-export to Markdown report
python main.py --output ~/report.md         # Export to specific path
python main.py --pull llama3.2:latest       # Pull a model directly
python main.py --no-pull-prompt             # Skip interactive pull prompt
python main.py --config                     # Show current config
python main.py --config-set offline_mode=true  # Set a config value

See docs/USAGE.md for the full guide with platform-specific notes and FAQ.

Features

• Interactive mode — Guided step-by-step session when run with no arguments
• Hardware detection — GPU VRAM, CPU, RAM on Windows, macOS, Linux
• Apple Silicon support — Treats unified memory as VRAM for accurate scoring
• Live + offline modes — Fetches from Ollama API or uses built-in fallback list
• Smart recommendations — Full GPU / partial CPU+GPU offload / CPU-only scoring
• Use-case grouping — Coding, Reasoning, Chat
• Benchmark estimates — Rough tokens/sec estimation per model
• Model detail view — Deep dive into a specific model's variants and compatibility
• Already-pulled detection — Highlights models you've downloaded
• Auto-pull — Pull a recommended model interactively
• Markdown export — Save results as a formatted report
• Config file — Persistent defaults via ~/.ollama-scout.json

Requirements

Package	Purpose
rich	Terminal UI (tables, panels, spinners)
requests	Fetch Ollama library API
psutil	Cross-platform RAM detection

Roadmap

• Config file support (~/.ollama-scout.json)
• pip installable package (ollama-scout CLI command)
• GPU benchmark integration (real ollama run timing)
• Model comparison mode (side-by-side two models)
• XDG config path support (~/.config/ollama-scout/)
• Web UI version

Contributing

PRs welcome! Especially for:

• Better use-case mapping for new models
• Multi-GPU scoring improvements
• Additional platform testing (Windows ARM, Linux ARM)

License

MIT