CanIRun.ai

The best AI models for your machine — recommended in seconds.

Your browser detects your CPU, RAM and GPU automatically, then we recommend the top open models to run locally for each use case: coding, chat, reasoning and vision.
No installs, no benchmarks, no guesswork.

canirun.ai · Report Bug · Request Model

Why

Cloud AI APIs are expensive, rate-limited, and send your data to third parties. Running models locally gives you privacy, speed, and zero cost per token — but only if your hardware is up to the job.

CanIRun.ai answers that question instantly. Open the site, let it detect your hardware, and get a curated set of best-pick recommendations grouped by use case (general, coding, reasoning, vision and lightweight) — the top open models that actually run well on your device. Prefer to explore? Switch to Browse all for the full compatibility report across 55+ open-weight models with grades from S to F.

How It Works

Browser APIs → Hardware Detection → Per-Use-Case Ranking → Best-Pick Recommendations

1. Hardware detection runs entirely client-side using WebGL, WebGPU, navigator.deviceMemory and a lightweight CPU micro-benchmark.
2. Each model's VRAM requirements are calculated across 7 quantization levels (Q2_K → F16) from parameter count.
3. A scoring algorithm combines run status, estimated tokens/second, memory headroom and model size into a letter grade (S–F).
4. Results are displayed instantly — nothing is sent to any server.

Supported hardware

Features

• Best-pick recommendations — the top open models for your device, grouped by use case (general, coding, reasoning, vision, lightweight), quality-ranked but gated by what actually runs well
• Zero-install hardware detection — CPU cores, RAM, GPU model, VRAM and memory bandwidth identified from the browser
• 55+ curated open models — from Qwen 3 0.6B up to GLM-5.2 753B and Kimi K2.6 1T, pruned to the ones worth running (no stale duplicates)
• 7 quantization levels per model — Q2_K, Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0, F16 with computed VRAM sizes
• S–F grading system — instant letter grade based on your hardware vs. model requirements
• Tokens/second estimates — approximate inference speed from memory bandwidth data
• Filters — by use case (chat, code, reasoning, vision), provider, architecture (dense / MoE), features (tool use, thinking)
• Search & keyboard shortcuts — / to search, j/k to navigate, Enter to open, v to switch view
• Three view modes — compact grid, detailed grid, and list
• Tier list — shareable S–F tier list you can export as an image
• Model detail pages — per-quant compatibility table, one-click Ollama / LM Studio / llama.cpp install commands
• OG images — dynamically generated social preview images for every model
• SEO — Schema.org structured data, sitemap, semantic HTML
• View Transitions — smooth page animations via Astro Client Router

Model Catalog

Models from Meta, Google, Alibaba, DeepSeek, Mistral AI, Microsoft, NVIDIA, Liquid AI, Z.ai, Moonshot AI, OpenAI and the community:

API

The same compatibility engine that powers the site is exposed as a small JSON API, so you can integrate CanIRun.ai into dashboards, PC configurators, CLI tools or custom assistants. All endpoints are CORS-enabled and return application/json.

Hardware profile — all POST endpoints accept a hardware object. Provide a GPU name and we enrich VRAM/bandwidth from the internal database, or pass explicit values:

{
  "hardware": {
    "cpu": { "name": "AMD Ryzen 7 5800X", "cores": 8, "threads": 16 },
    "ramGb": 32,
    "gpu": { "name": "NVIDIA RTX 3060", "vramGb": 12, "memoryBandwidthGbps": 360 }
  }
}

Apple Silicon ("gpu": { "name": "Apple M3 Max" }) is detected automatically and treated as unified memory. Omit gpu for a CPU / integrated-GPU profile.

POST /api/compatibility

curl -X POST https://canirun.ai/api/compatibility \
  -H 'content-type: application/json' \
  -d '{ "hardware": { "ramGb": 32, "gpu": { "name": "NVIDIA RTX 3060" } },
        "modelId": "llama-3.1-8b", "quantization": "Q4_K_M" }'

{
  "compatible": true,
  "status": "comfortable",          // comfortable | tight | cpu-offload | insufficient | unknown
  "grade": "A",                     // S–F
  "score": 82,
  "modelId": "llama3.1-8b",
  "quantization": "Q4_K_M",
  "recommendedQuantization": "Q8_0",
  "estimated": {
    "tokensPerSecond": 55,
    "modelSizeGb": 3.9,
    "vramRequiredGb": 4.6,
    "ramRequiredGb": 7.5,
    "memoryHeadroomGb": 7.4
  },
  "notes": ["The model should fit comfortably in GPU memory.", "..."]
}

quantization is optional — when omitted, the best-fitting quant is used.

POST /api/recommend

curl -X POST https://canirun.ai/api/recommend \
  -H 'content-type: application/json' \
  -d '{ "hardware": { "ramGb": 64, "gpu": { "name": "RTX 4090", "vramGb": 24 } },
        "useCase": "code", "limit": 5 }'

Returns a ranked recommendations array (each with quant, grade, status and estimated tokens/second). useCase and limit (1–25, default 5) are optional.

Tech Stack

Getting Started

Prerequisites: Node.js 18+ and pnpm

# Clone the repo
git clone https://github.com/midudev/canirun.ai.git
cd canirun.ai

# Install dependencies
pnpm install

# Start dev server
pnpm dev

Open localhost:4321 to see the site.

Commands

Project Structure

packages/
├── models/
│   └── src/index.ts        # 90+ AI model definitions with quant calculations (edit here)
├── compatibility/
│   └── src/index.ts        # GPU/Apple/Mobile DBs + compatibility scoring (edit here)
└── runai/                   # CLI for running models locally

src/
├── data/
│   ├── models.ts            # Re-exports @canirun/models (built from packages/models)
│   └── hf-stats.json        # HuggingFace download/like counts
├── lib/
│   ├── hardware.ts         # Re-exports @canirun/compatibility
│   └── og.ts               # OG image generation utilities
├── pages/
│   ├── index.astro         # Home — model grid with filters & search
│   ├── tier.astro          # Tier list — S–F ranking with image export
│   ├── model/[id].astro    # Model detail — quants, compatibility, install
│   ├── api/                # JSON API — models, compatibility, recommend
│   └── og/                 # Dynamic OG image endpoints
├── components/
│   └── NavHeader.astro     # Site navigation
├── layouts/
│   └── Layout.astro        # Base layout with SEO, fonts, transitions
├── icons/                  # SVG icon components
└── styles/
    └── global.css          # Theme tokens, Geist fonts, dark mode

Contributing

Contributions are welcome! Some ways to help:

• Add a model — add an entry to the STATIC_MODELS array in packages/models/src/index.ts following the existing pattern (the AIModel interface at the top of that file).
• Improve hardware detection — extend the GPU/Apple/Mobile databases in packages/compatibility/src/index.ts
• Report inaccurate results — open an issue with your hardware info and the model in question
• Fix bugs or improve UI — PRs are appreciated

Author

Created by midudev · @midudev

License

MIT