Parallax Compute
Decentralized P2P GPU inference network
A peer-to-peer network where anyone can contribute GPU compute for AI inference, earning credits they can spend on inference themselves. Implements pipeline sharding to split large models across multiple consumer GPUs for faster inference.
Inference demand keeps rising, but small teams often cannot afford dedicated GPU infrastructure. I wanted to explore whether distributed consumer hardware could become a credible product surface.
Shaped the product concept, economics, systems architecture, and trust model for a decentralized inference network.
System Constraints
Heterogeneous hardware across peers
Verification and trust in a distributed compute marketplace
Latency overhead introduced by sharding and network hops
Need for an interface developers could adopt without learning a new stack
What I Built
A peer-to-peer inference concept built around pipeline sharding across consumer GPUs.
A verification and reputation model using commit-reveal patterns and credits-based incentives.
An OpenAI-compatible access layer so the network could be evaluated as a practical developer product.
Outcome / Takeaway
Framed decentralized inference as a platform product rather than just a protocol experiment.
Explored how architecture, incentives, and developer experience have to work together for the concept to be usable.
Added a compute story to the wider Parallax ecosystem that went beyond analytics and intelligence tooling.
Interesting infrastructure products live at the intersection of systems design and adoption design. If the developer experience is weak, the architecture never gets a chance.