Run one model across several devices. Each device downloads and holds only its own layers — straight from Hugging Face, never from a peer — and passes the hidden state around the ring over WebRTC. The group can run a model that no single device could hold. Every multiply goes through the verified INT8 units. Runs locally: npm start.
Any Hugging Face repo with .safetensors weights whose architecture is Llama-style (Llama, Mistral, Qwen2, SmolLM, TinyLlama) or GPT-2-style. Loading reads only config.json, the tokenizer, and the weight headers — a few tens of KB, whatever the model's size. Gated or private repos will ask for a token, once.
load a model to see how it would be split
Layers are apportioned by measured speed — each device times a real GEMM through its own kernel. The MB figure is what that device will actually download and hold.
There is no KV cache — every token re-runs the whole window, so context length is the main cost per token.
0 = greedy, which is deterministic and therefore checkable. Above 0, sampling follows the seed so runs stay reproducible.
JSON constrains the completion only. The mask checks the top 40 tokens. The hidden state stays the same. Above temperature 0, the seed is tied to the token position.
Needs a secure context (localhost or HTTPS) for WebGPU and cross-device WebRTC. No WebGPU? The identical verified INT8 units run on CPU — slower, but a CPU device and a GPU device produce the same bits, so they can share a ring. There is no plain-float path.
Heads up: peers connect directly, so devices in your ring can see each other's IP address. A middle stage never receives the embedding table or the vocabulary, but it does see the hidden states passing through — and the head sees your prompt and output. Run rings with devices and people you trust. Proof of concept, not a hardened service.
This model needs a token to download.
Kept in memory for this tab only: never written to disk or storage, never put in a URL, never logged, and never sent to other devices — each device is asked for its own. Create a read token at huggingface.co/settings/tokens.