ParallelOS turns thousands of idle GPUs into one programmable supercomputer — orchestrating AI inference, distributed training and massive batch pipelines across a global node network.
A central Core System decomposes and schedules every job; a worldwide Node Layer contributes GPU power and executes work in parallel.
Receives user jobs, analyzes execution requirements, decomposes workloads, selects nodes, routes data, handles fault tolerance and assembles the final result. It never computes — it optimizes the whole network.
Each node contributes GPU memory, compute, bandwidth and uptime. Nodes receive assigned work units, execute them independently or in pipeline, return outputs and continuously report health.
Job is parsed to determine its compute structure & dependencies.
Decomposed into smaller independent work units.
Units routed to the best-fit nodes across the network.
Nodes process in parallel or pipeline, exchanging intermediate data.
Outputs collected & assembled into one final result.
Any workload that can be divided into independent units thrives on ParallelOS.
Serve models at scale with low-cost distributed inference across thousands of nodes.
Multiple nodes jointly optimize a model using data & pipeline parallelism.
Fan out massive batch jobs and collect results as units complete.
Generate large synthetic datasets in parallel for training pipelines.
Compute embeddings over enormous corpora at network scale.
Run sweeping evals & benchmarks across distributed compute.
Instead of one expensive machine, ParallelOS aggregates compute from many connected nodes and coordinates execution through a central layer — scaling with the size of the network itself.
Node onboarding, Core System orchestration & the foundational compute fabric.
Submit inference jobs, price compute, settle in $PLOS.
Data & pipeline-parallel training across many nodes.
Reputation scoring, staking & refined operator rewards.
SLAs, private clusters & enterprise-grade compute access.