@tsany/whollama

whollama

Find the best Ollama model for your hardware, ranked by real benchmarks.

Auto-detect your GPU/CPU/RAM and get ranked recommendations from the Ollama library — scored by real benchmark data, not just parameter count.

Quick Start

npx @tsany/whollama

That's it. whollama detects your hardware, fetches the latest model catalog and benchmark scores, and shows you the top recommendations.

Install

npm install -g @tsany/whollama   # global install
# or
npx @tsany/whollama              # zero-install, always latest

Usage

whollama                  # Show top 10 recommended models
whollama --top 5          # Show top 5
whollama --task coding    # Filter by task (coding, vision, math, general)
whollama --json           # JSON output for scripting
whollama --offline        # Force offline mode
whollama --verbose        # Show scoring breakdown

whollama pull             # Interactive model pull
whollama pull qwen3:14b   # Pull a specific model

whollama list             # List all fitting models
whollama list --all       # List all models (including non-fitting)

whollama info qwen3:14b   # Detailed model info

whollama update           # Force refresh catalog and benchmarks

Features

• Hardware auto-detection — Apple Silicon, NVIDIA, AMD, or CPU-only
• Live catalog — scrapes ollama.com/library for the latest models
• Multi-source benchmarks — LiveBench, Chatbot Arena ELO, Open LLM Leaderboard
• Smart scoring — composite score factoring benchmark quality, VRAM fit, speed, and recency
• Offline mode — bundled fallback catalog and scores work without internet
• JSON output — pipe-friendly for scripts and automation
• Zero config — no API keys, no setup, no accounts

How It Works

1. Detect — GPU, VRAM, RAM, disk, and OS are auto-detected
2. Fetch — Model catalog from Ollama + benchmark scores from multiple sources
3. Score — Each model is ranked by a weighted composite of benchmark quality, VRAM fit, estimated speed, and recency
4. Display — Top recommendations shown in a formatted table, or as JSON

Output

╭─────────────────────────────── Hardware ───────────────────────────────╮
│ GPU: Apple M1 Pro — 16 GB unified  BW: 200 GB/s                        │
│ RAM: 16 GB  •  Disk: 450 GB free                                        │
╰─────────────────────────────────────────────────────────────────────────╯

  Recommended Models (task: general)

  #   Model               Params   Quant     Score   Speed     Tags
  1   qwen3:14b           14.8B    Q4_K_M    87.4    22 t/s    tools
  2   gemma4:12b          12.0B    Q4_K_M    84.1    28 t/s    vision
  3   llama3.2:3b          3.2B    Q8_0      71.2    65 t/s    tools
  ...

  Top pick: qwen3:14b  •  Run: ollama pull qwen3:14b

Why whollama?

Running local LLMs via Ollama requires answering two questions that are hard to answer together:

1. What can my hardware run? — VRAM, RAM, and disk constrain viable models
2. Which of those is actually the best? — Parameter count is a poor proxy for quality

whollama answers both in a single command.

License

MIT