Aurora Helm · GPU inference fleets
Predict demand. Prep models. Hit your latency targets — within your power cap.
Aurora Helm is fleet control for GPU inference: it sees what's coming, gets models ready, and schedules work so you don't trip breakers or miss SLOs.
The problem
Traffic spikes and power caps shouldn't fight each other.
Most inference fleets pick one. They prewarm aggressively and blow power budgets, or they conserve power and suffer p99 latency when traffic surges. Neither is acceptable at scale.
Cold-start latency
Models that aren't warm when demand hits create p99 spikes that degrade user experience and violate SLOs — often invisibly until a major event.
Power cap violations
Prewarming draws significant power. Warming multiple models simultaneously can breach site capacity limits — tripping breakers or triggering emergency throttling.
No tool does both
Most schedulers optimize latency or power independently. No single system manages warmth inventory against a real watt constraint — until now.
What Helm does
One plan for time and watts.
01
Stay fast when traffic spikes
Helm reads fleet signals — current traffic, queue depth, historical patterns — to forecast demand and get models ready before the wave hits.
02
Stay within your power budget
Warming tasks are scheduled so peak power draw stays within your declared site capacity. Watts are a first-class constraint, not an afterthought.
03
Models ready before demand hits
Model weights, caches, and routing paths are staged ahead of demand — so requests land on a ready system, not one in the middle of loading.
04
Built-in token efficiency
The platform recovers stranded host cycles during inference, reducing compute per token. Same model outputs — fewer cycles per request.
Who buys
Built for fleet operators.
Helm is for teams running GPU inference at scale — frontier labs, hyperscalers, GPU clouds, and AI factories managing multi-rack serving infrastructure running vLLM, TensorRT-LLM, SGLang, or similar frameworks.
How to start
Pilot first, automate when ready.
-
1Pilot with telemetry Days
Connect your existing fleet signals — DCGM, Prometheus, Run:ai, or vendor APIs. See demand forecasts and recommendations before Helm controls anything.
-
2Enable advisory mode Weeks
Helm produces schedules and explains every decision. Your team reviews and approves. Nothing runs automatically.
-
3Turn on automation Your call
When you're confident in the forecasts and schedules, enable automated preparation. Off by default — always your choice.
Transparency note
Helm GPU fleet features are in pilot and projection phase. Full end-to-end GPU benchmarks are still in progress. Fleet automation is off by default until your team enables it. We will not claim guaranteed speedup multipliers we haven't measured.
Ready to run a pilot?
Talk to our engineering team about your fleet and what a Helm pilot looks like.