Hi HN! I’ve been building Shard, a browser-powered distributed AI inference network designed to let users contribute compute (via WebGPU) while powerful verifier nodes finalize outputs.
What it is right now
Shard is a functioning early-stage system that lets:
• Browsers act as Scout nodes to contribute WebGPU compute
• A libp2p mesh for P2P networking
• Verifier nodes run stronger local models to validate and finalize inference
• A demo web app you can try live today
• Clients fall back gracefully if WebGPU isn’t available
• Rust daemon + Python API + web UI all wired together
It’s essentially a shared inference fabric — think distributed GPU from volunteers’ browsers + stronger hosts that stitch results into reliable responses. The repo includes tooling and builds for desktop, web, and daemon components.
Why it matters
There’s a growing gap between massive models and accessible compute. Shard aims to:
• Harness idle WebGPU in browsers (scouts)
• Validate and “finish” results on robust verifier nodes
• Enable decentralized inference without centralized cloud costs
• Explore community-driven compute networks for AI tasks
This isn’t just a demo — it’s a full stack P2P inference system with transport, networking, and workflow management.
Current limitations
• Early stage, not production hardened
• Needs more tests, documentation, and examples
• Security and incentive layers are future work
• UX around joining scheduler/mesh could improve
Come build with me
If you’re into decentralized compute, AI infrastructure, web GPU, or mesh networks — I’d love feedback, contributions, and ideas. Let’s talk about where shared inference networks could go next.
What it is right now
Shard is a functioning early-stage system that lets: • Browsers act as Scout nodes to contribute WebGPU compute • A libp2p mesh for P2P networking • Verifier nodes run stronger local models to validate and finalize inference • A demo web app you can try live today • Clients fall back gracefully if WebGPU isn’t available • Rust daemon + Python API + web UI all wired together
It’s essentially a shared inference fabric — think distributed GPU from volunteers’ browsers + stronger hosts that stitch results into reliable responses. The repo includes tooling and builds for desktop, web, and daemon components.
Why it matters
There’s a growing gap between massive models and accessible compute. Shard aims to: • Harness idle WebGPU in browsers (scouts) • Validate and “finish” results on robust verifier nodes • Enable decentralized inference without centralized cloud costs • Explore community-driven compute networks for AI tasks
This isn’t just a demo — it’s a full stack P2P inference system with transport, networking, and workflow management.
Current limitations • Early stage, not production hardened • Needs more tests, documentation, and examples • Security and incentive layers are future work • UX around joining scheduler/mesh could improve
Come build with me
If you’re into decentralized compute, AI infrastructure, web GPU, or mesh networks — I’d love feedback, contributions, and ideas. Let’s talk about where shared inference networks could go next.
Repo: https://github.com/TrentPierce/Shard