High-Performance Computing & Distributed Inference
Distributed AI inference at scale. Run foundation models across heterogeneous compute clusters with automatic sharding, load balancing, and fault tolerance.
Shard large models across multiple GPUs, nodes, and even edge devices with sub-100ms overhead.
Dynamic resource allocation based on demand. Scale from zero to thousands of GPUs seamlessly.
Route requests to the optimal model variant based on latency, cost, and quality requirements.
Marketplace for spare GPU capacity. Monetize idle compute or access affordable inference.