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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 see a personalized compatibility report for 68+ open-weight models with grades from S to F.

How It Works

Browser APIs → Hardware Detection → Model Matching → Personalized Grades

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

Platform	Detection method
NVIDIA RTX 30xx / 40xx / 50xx, A100, H100	WebGL renderer string + GPU database
AMD RX 6xxx / 7xxx / 9xxx	WebGL renderer string + GPU database
Intel Arc A-series	WebGL renderer string + GPU database
Apple Silicon M1–M4 (Pro, Max, Ultra)	WebGL + unified memory lookup
Mobile (iOS / Android)	Screen resolution, benchmark, Adreno/Mali/Immortalis DB

Features

• Zero-install hardware detection — CPU cores, RAM, GPU model, VRAM and memory bandwidth identified from the browser
• 68+ AI models — from TinyLlama 1.1B to Llama 4 Maverick 128E and Qwen3 Coder 480B
• 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 and the community:

Family	Models
Llama	3.1 8B, 3.1 405B, 3.2 1B/3B/11B-Vision, 3.3 70B, 4 Scout, 4 Maverick
Qwen	2.5 7B–72B, 2.5 Coder, 3 1.7B–235B, 3.5 0.8B–397B, 3 Coder 480B
Gemma	2 2B/9B/27B, 3 1B/4B/12B/27B
DeepSeek	R1 1.5B–32B, V3.1, V3.2
Mistral	7B, Nemo 12B, Small 24B, Mixtral 8x7B/8x22B, Devstral
Phi	3.5 Mini, 4 14B, 4 Mini Reasoning
Others	Nemotron, GLM-4, OLMo 2, SmolLM3, LFM2, EXAONE, Kimi K2, GPT-OSS

Tech Stack

	Technology	Purpose
🚀	Astro 5	Static site generation with islands architecture
🎨	Tailwind CSS 4	Utility-first styling
🔤	Geist	Sans, Mono and Pixel typefaces
🖼️	Satori + resvg	OG image generation (JSX → SVG → PNG)
📸	@zumer/snapdom	Tier list export to image
🗺️	@astrojs/sitemap	Automatic sitemap generation

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

Command	Action
pnpm dev	Start dev server at localhost:4321
pnpm build	Build production site to ./dist/
pnpm preview	Preview production build locally
pnpm scrape	Fetch model stats from HuggingFace

Project Structure

src/
├── data/
│   ├── models.ts          # 68+ AI model definitions with quant calculations
│   └── hf-stats.json      # HuggingFace download/like counts
├── lib/
│   ├── hardware.ts         # Client-side hardware detection engine
│   └── 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
│   └── 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 src/data/models.ts following the existing pattern
• Improve hardware detection — extend the GPU/Apple/Mobile databases in src/lib/hardware.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