README.md

---

title: DaisyChain-Infer
emoji: ❄️
colorFrom: blue
colorTo: gray
sdk: docker
app_port: 7860
license: mit
short_description: One model split across your devices, P2P in the browser
hf_oauth: true
hf_oauth_scopes:
  - read-repos
hf_oauth_expiration_minutes: 480
---


# ❄ DaisyChain-Infer — one model, split across your devices

Part of **DaisyChain** → https://huggingface.co/DaisyChainAI

Pick a Hugging Face model, open this page on two or more of your devices, and
each one takes a **slice of the model's layers**. A token is produced by passing
the hidden state around the ring, peer-to-peer over WebRTC. Every multiply runs
through **verified INT8 units** — WebGPU where available, the identical math on
CPU where not.

The group can run a model that **no single device could hold**, because each
device downloads only its own layers.

> **Project files, guide and test suites:**
> https://huggingface.co/DaisyChainAI/DaisyChain-Infer

## How to use it

1. **Create a room** and open the invite link on your other devices. You approve
   each device before it joins. (There is no automatic grouping here — see
   *Privacy* below.)
2. On **one** device, type a model repo id and press **Load**. That device
   becomes the **head**.
3. Press **Generate**. Each device fetches its own layers from the Hub, reports
   ready, and the ring starts.

Models it can run: any `.safetensors` repo that is **Llama-style** (Llama,
Mistral, Qwen2/2.5, SmolLM, TinyLlama) or **GPT-2-style**. Try
`HuggingFaceTB/SmolLM-135M`, `openai-community/gpt2`, or `Qwen/Qwen2.5-0.5B`.

Anything else is **refused by name**, not approximated — running an unknown
block shape through these kernels would produce fluent, confident, wrong text.

## Nothing is downloaded to this Space

This Space is a **WebSocket signaling server and a static file host**. It never
sees a weight, an activation, your prompt, or the generated text. Weights go
from the Hugging Face CDN **straight to each browser**, using HTTP range
requests against the byte offsets in the safetensors header — so a device
transfers only the layers it owns, and pressing *Load* costs a few tens of KB
regardless of the model's size.

## Signing in

Public models need no sign-in at all.

For **gated or private** models, use **Sign in with Hugging Face**. You are
redirected to huggingface.co and back, and this page receives a scoped,
expiring token (read access to repositories, nothing more) held **in memory for

that tab only** — never written to storage, never logged, and never sent to
another device. Each device signs in for itself, because each device downloads
its own layers.

There is deliberately **no box to paste a personal access token** here. Typing
a PAT into a page served by someone else is a bad habit even when the code is
honest, because you cannot verify that it is. If you would rather not sign in at
all, clone the project and run it locally — the local build accepts a token
directly, because there the page is served from your own machine.

One honest caveat: the OAuth code exchange requires a client secret, so it
happens on the server, which means this Space briefly handles your access token
during that exchange. It is not stored or logged, and it is handed to your
browser in the URL fragment (which browsers never send to servers) and stripped
from history on arrival. That is the smallest exposure the flow allows — it is
not zero.

## Privacy and trust

- **Rooms are private by invitation.** This deployment disables the LAN
  auto-grouping the local build uses, because on a public URL two strangers
  behind the same CGNAT or on the same campus network share a public IP and
  would otherwise be placed in one ring together.
- **Peers connect directly**, so devices in your ring can see each other's IP
  addresses.
- A middle stage never receives the embedding table, so **it never sees the

  vocabulary** — it passes floats it cannot interpret. But it does see the
  hidden states going through it, and the head sees your prompt and output.
- **Activations are not authenticated.** A malicious stage that runs correct
  arithmetic but returns a crafted activation is not caught by anything here.
  The verification proves the *computation* is right on every honest device;
  trust in the participants is yours to establish.

**Ring only with devices and people you trust. This is a proof of concept, not

a hardened service.**

## Honest limits

- **Latency, not bandwidth, is the cost.** Every token pays one round trip per
  stage, so tokens/sec *falls* as you add devices. More stages buy capacity, not
  speed.
- **No KV cache** — every token re-runs the whole window, which is also what
  keeps a split run bit-comparable against an unsplit one.
- **Weights are held as f32**, so budget ~4 bytes per parameter across the
  group. The ring plan shows each device's cost before you start.
- **Needs WebGPU or a patient CPU**, and a secure context (this Space is HTTPS,
  so that is fine).
- The **head is a single point of failure**; a stage that drops stalls the ring.
  Press Generate again to re-plan around whoever is still connected.

---

**License:** MIT · **Author:** Dean Byrne (Quazim0t0) · **Org:** DaisyChainAI

Built on [DaisyChain-Train](https://huggingface.co/DaisyChainAI/DaisyChain-Train)
and [DaisyChain-Web](https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web) —
the verified INT8 units, the WGSL kernels and their exact gates, and the WebRTC
mesh are carried over unchanged.