Deploymodels.
Ownthestack.
Deployopen-sourceandproprietarymodelsviamanagedAPIs,runprivateLLMsondedicatedinfrastructure,andaccessrawGPUcapacityexactlyhowyourworkloaddemandsit.
AI FactoryInference Engine9IEModel APIs
Tokens As A Service
Call open-source and proprietary models over a standard API. Pay per token, no baseline fees, no infrastructure to provision.
Private LLMs
Dedicated Model Hosting
Serve models on dedicated, isolated infrastructure for deterministic latency, absolute data residency, and total configuration control.
Provisioned Output
Reserved Throughput
Commit to a throughput tier for guaranteed token output capacity. No cold starts, no shared-queue contention at peak load.
Serving Infrastructure
Zero-Ops Model Serving
Autoscaling, load balancing, and health checks handled by the platform. Deploy a model, get an endpoint.
GPU as a Service9GSGPU Virtual Machines
GPU-attached VMs with direct hardware access. Run any framework or driver stack, no platform restrictions.
MIG Slices
Partitioned GPU capacity via Multi-Instance GPU. Right-sized for inference and fine-tuning without a full card.
GPU Clusters
Multi-node clusters over high-bandwidth fabric. Built for distributed training across multiple machines.
Network Fabric9NFDistributed Training Fabric
Move data at training speed with high-bandwidth, high-radix networking that keeps multi-node training synchronized.
Low-Latency GPU Interconnect
Tight coupling within compute with ultra-low latency connectivity within racks for fast GPU-to-GPU communication.
Inference-Optimized Routing
Session-aware routing across replicas for streaming and long-lived connections, with predictable latency under load.
Built by experts
