Private inference network

The model is public.The prompt is not.

Decentralized inference on contributed GPUs. Our research goal: a network where an untrusted node cannot recover what you typed.

Share lattice · Game of Life

01 / The problem

Sharding solved size. Not privacy.

Split a model across strangers' GPUs and every node still touches raw activations - enough to reconstruct pieces of a prompt.

02 / How it leaks

A request crosses machines you don't control.

promptoutputEntrylayers 0-15exposedNode 2layers 16-31Node 3layers 32-47Exitlayers 48-63exposed

Every node handles real activation tensors. SILQ exists to quantify that exposure - and close it.

The measured one

Private

A raw hop still reconstructs the prompt. One MPC share is noise. The bound costs 2.42 MiB per token. The figure is in the Paper.

Decentralized

Anyone's GPU, pooled into swarms. No datacenter.

POLICYQWENLLAMAANYMODEL

Uncensored

Any model. No content-policy layer in the way.

Privacy isn't a feature to bolt on later. It's the open problem - and the one SILQ exists to close.

03 / Public vs private

Public models. Private prompts.

RECOVERY
APIhosted100%logged
WEIGHTSlocal100%raw
OBFUSCopen100%invertible
SHARDmulti94-100%activations
SILQ2PC0.21%chance

04 / Product

Three ways into the network

Recommended

Native Worker

Background GPU via Ollama. Larger models, higher pay per job.

Paid per job

npx -y @silqxyz/worker@0.2.0 --token YOUR_TOKEN
Get install command
Zero install

Browser Worker

In-tab inference via WebGPU. One click, no terminal.

Paid per job (lower)

Open browser worker
User side

OpenAI-compatible API

Drop-in chat completions. We don't store prompts; 1 credit per request.

1 credit / request

curl https://api.silq.example/v1/chat/completions
Try the playground

Private inference economy

The $SILQ token

What crosses the chain is payment. Your prompts never go on-chain.

01

Metered, not monitored

We keep no prompt history or profiles. In v1 the serving worker sees the prompt.

02

Treasury funds privacy

The margin on every paid job funds the network, its privacy research and $SILQ buybacks.

We give a FAQ

SILQ