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metadata
title: Meditor WebGPU Chat
emoji: 🧠
colorFrom: green
colorTo: indigo
sdk: static
pinned: false
license: apache-2.0
short_description: Run Meditor MoE & Dense LLMs 100% in-browser on WebGPU
🧠 Meditor WebGPU — Client-Side In-Browser AI Chat
An interactive, high-performance web chat space running Meditor models directly in your browser using WebGPU hardware acceleration.
- ⚡ Zero Cloud GPU Costs: The neural network weights are downloaded into your browser and executed on your local GPU via WGSL shaders.
- 🔒 100% Private: Your prompts and generated text never leave your computer.
- 💾 Persistent OPFS Caching: Model weights are cached locally via the browser's Origin Private File System (OPFS); once downloaded, the model boots instantly on subsequent visits without re-downloading.
- 🎛️ Full Telemetry: Live tokens per second (
tok/s), time-to-first-token (TTFT), token counts, and GPU device details.
🚀 Available Meditor Models
| Model | Parameters | Quantization | Size | Description |
|---|---|---|---|---|
| Meditor-1 Universal | 500M Dense | Q4_K_M (4-bit) |
~468 MB | ⚡ Recommended: Instant download, 60–150 tok/s on WebGPU. Great for mobile & laptops. |
| Meditor-1 Universal | 500M Dense | Q8_0 (8-bit) |
~644 MB | High-fidelity 8-bit quantization. |
| Meditor-1 Universal | 500M Dense | BF16 (16-bit) |
~1.18 GB | Uncompressed reference weights. |
| Meditor-2 Universal | 3.4B MoE (1.8B Active) | Q3_K_M (3-bit) |
~1.60 GB | 22-expert MoE architecture, fast multi-turn reasoning. |
| Meditor-2 Universal | 3.4B MoE (1.8B Active) | Q4_K_M (4-bit) |
~2.00 GB | Balanced 4-bit MoE with dialogue, reasoning, and coding expertise. |
| Meditor-2 Universal | 3.4B MoE (1.8B Active) | Q5_K_M (5-bit) |
~2.33 GB | High-fidelity 5-bit MoE. |
| Custom HF Model | Any GGUF | Any | Variable | Load any custom repository and GGUF file from Hugging Face Hub. |
🛠️ Browser Requirements
- Google Chrome / Chromium / Edge / Brave (v113+) — Native WebGPU enabled by default.
- Safari (v17+) — WebGPU supported.
- Firefox (v115+) — WebGPU can be enabled via
about:config(dom.webgpu.enabled = true).