An orchestration engine built for scale, precision, and real workloads.
Inference. Training. Media generation. Agents. Pipelines. Unified under one runtime.
One Interface. Infinite Execution Paths.
Hyper abstracts the complexity of running AI workloads:
Containerized execution. Model runtime acceleration. Distributed scheduling. GPU multiplexing. Memory-efficient loading. Intelligent caching.
Your code becomes a deployable artifact.
Hyper handles everything else.
A Low-Latency, High-Throughput Orchestrator.
The scheduler continuously evaluates:
GPU availability
Model placement
Parallelizable segments
Token throughput
Latency targets
Failure domains
Workloads are dynamically routed to the optimal hardware in milliseconds.
A virtualized grid of GPUs.
Elastic. Heterogeneous. Everywhere.
Hyper supports:
H100 / A100 / H200 / B200. L40S / L4 / older architectures. Mixed fleets. Shared nodes. Fractional GPUs. Multi-DC routing.
The physical limits disappear.
The fabric handles the rest.
Performance through specialization.
Hyper integrates the best model-acceleration stacks:
vLLM
SGLang
TensorRT-LLM
GGUF / AWQ / GPTQ
Speculative decoding
Quantization-aware workloads
Everything runs at peak efficiency.
Without manual tuning.
Predictive scaling for unpredictable workloads.
Hyper anticipates demand:
Pre-warms models
Manages cold starts
Predicts concurrency
Allocates fractional GPUs
Prunes idle nodes
Stabilizes latency
Capacity grows and contracts with workload pressure.
Cloud. On-prem. Air-gapped.
One runtime across all environments.
The architecture supports:
Private GPU clusters. Sovereign deployments. Regulated workloads. Multi-region failover. Zero-trust boundaries.
Every environment behaves like part of the same system.
Visibility into every token and thread.
Job timelines
Resource allocation
Latency breakdowns
Model logs
GPU metrics
Cost insights
The fabric is opaque to manage, but transparent to observe.
