Products

SwarmOrchestrator.

Imagine a 1,024-GPU cluster split across tenants and models. It’s a mess. Some nodes overloaded, some idle. Each model has different compute profiles, batch sizes, latency requirements. Manual placement and static allocation leave 20–40% of capacity wasted. Enter: SwarmOrchestrator

SwarmOne boosted personnel efficiency by about 90%, significantly reduced training costs, and enhanced delivery, making us far more competitive in our market.

Dr. Michael Erlihson
Dr. Michael Erlihson
AI Tech Lead, Salt Security

Core

Production-Grade Orchestration

Multi-Node Workload Placement

Intelligent placement across your entire cluster. SwarmOrchestrator right-sizes resources per model, not per tenant - maximizing utilization per GPU-hour across hundreds or thousands of nodes.

Multi-Tenant Isolation

Every tenant gets their SLO enforced independently. Production workloads are never affected by batch jobs. Full isolation with shared infrastructure economics.

Model-Specific Optimization

Each model has different compute profiles, batch sizes, and latency requirements. SwarmOrchestrator optimizes for each model's characteristics - not one-size-fits-all static allocation.

Operations

Intelligent Infrastructure

SLO-Driven Autoscaling

Define your SLO - latency, throughput, or cost - and SwarmOrchestrator enforces it automatically. Scales up or down based on real targets, not static thresholds.

Scale-to-Zero

No GPU burns without earning. Idle workloads scale to zero automatically. When demand returns, SwarmOrchestrator spins resources back up within your SLO window.

Dynamic Rebalancing

As traffic shifts across tenants, SwarmOrchestrator rebalances GPU allocation between models and inference phases in real-time. No manual intervention required.

Any Chip Architecture

NVIDIA, AMD, Tenstorrent - SwarmOrchestrator works on any silicon. One orchestration interface regardless of the underlying hardware in your fleet.

Self-Improving Scheduler

Every inference job teaches it something. The scheduler builds a continuously improving model of your workload characteristics, becoming more efficient as you use it.

Hands-Off Failure Recovery

When hardware fails, SwarmOrchestrator checkpoints work and migrates seamlessly. Zero user interruption. Self-healing infrastructure that doesn't page your on-call team.

Outcomes

The Result

30% Utilization Improvement

Across existing hardware. No new GPUs needed. Pure margin expansion from intelligent placement and dynamic rebalancing.

Hours to Production, Not Months

Enterprise chip evaluations frequently stall at the deployment stage. SwarmOrchestrator provides the production layer that compresses the timeline from months to hours.

See SwarmOrchestrator in Action

Schedule a demo and see how SwarmOrchestrator transforms your GPU utilization and inference operations.