Decentralised GPU
Distributed GPU networks, compute marketplaces, and decentralised AI infrastructure.
Built on direct experience designing and shipping production decentralised GPU compute platforms — including P2P network stacks, verification protocols, and distributed inference systems.
Capabilities
What it takes to make decentralised compute production-grade.
Scheduling at the edge
- ·Globally distributed scheduling
- ·Latency-aware routing
- ·Spot and unreliable nodes
- ·Failure-domain modelling
P2P coordination
- ·Custom network stacks
- ·rust-libp2p integration
- ·Peer discovery
- ·Gossip and DHT designs
Trust & verification
- ·Trustless compute verification
- ·Result attestation
- ·Sampling and challenge protocols
- ·Reputation and scoring
Miner / validator infra
- ·Node software architecture
- ·Provisioning and updates
- ·Operator UX
- ·Slashing and incentive design
Metering & billing
- ·Usage capture
- ·Pricing and settlement
- ·On-chain / off-chain split
- ·Auditability
Distributed workloads
- ·Distributed inference
- ·Distributed training coordination
- ·DiLoCo-style training over WAN
- ·Result aggregation
Multi-tenancy
- ·Tenant isolation across operators
- ·Quotas and SLAs
- ·Confidential workloads
- ·Compliance posture
Observability
- ·Telemetry across non-uniform fleets
- ·Health scoring
- ·SLO measurement
- ·Operator dashboards
who.this.is.for
Useful for teams shipping real distributed compute.
Not a research exercise. These engagements ship code, harden protocols, and put decentralised compute into the hands of paying customers.
- 01GPU cloud startups
- 02Decentralised AI networks
- 03Compute marketplaces
- 04Bittensor-style infrastructure
- 05Research groups exploring distributed training
- 06Enterprises evaluating distributed compute economics
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