Autonomous AI
Resource Optimization

Elastic GPU capacity is only useful if it’s warm when the request arrives. Cold-starting a GPU node (image pull, driver init, model load into VRAM) can add minutes of latency, right when requests start to time out.

• Node Prewarmer anticipates demand and brings GPU nodes up ahead of the curve, not in reaction to it
• Models pre-load into VRAM before traffic arrives, so the first request hits a hot path
• Same logic applies to CPU-bound services with non-trivial startup
• Result: scale-to-zero economics without the cold-start latency tax

Optimizing GPU alone isn’t enough: a node out of ephemeral storage will starve its inference containers regardless of how much VRAM is free.

• Models utilization holistically across CPU, memory, ephemeral storage, network, GPU compute, GPU memory, and GPU power
• Recommends fractional sharing strategies and selects the right primitive per workload: timeslicing, MIG partitions, or MPS
• Targets the configuration where density and latency both improve, not maximum squeeze
• Net effect: concentrating jobs onto fewer, faster GPUs often lowers tail latency and raises yield together

• Fractioning produces the shapes; scheduling places them
• Places each workload’s fraction profile in real time, allocating exactly what’s needed and filling existing nodes before adding new ones
• Fills gaps as workloads start and stop, keeping GPUs in their high-yield band
• Outcome: real downward elasticity as demand drops, preventing low-yield nodes from running up the bill

Workload shapes drift: a container that fits cleanly at 14:00 may be pressuring its neighbors by 14:30.

• Detects compute, memory, and GPU pressure and moves containers to nodes where they’ll perform, without waiting for the next deploy
• Rebalancing is the corrective action of a real-time control plane, not a periodic cron
• Payoff: this self-correction is what makes aggressive density and sharing safe to run

Timeslicing is the cheapest way to share a GPU, and the most dangerous: by default, neighboring workloads share a memory address space.

• HAMi-core enforces per-workload memory boundaries on top of timeslicing, so density doesn’t come at the cost of safety
• Combined with dynamic rebalancing, enables aggressive sharing for production AI services that previously needed one-pod-per-GPU
• Bottom line: noisy neighbors stay in their lane; an OOM in one container doesn’t take down the others

GPU type and instance shape that’s cheapest for a workload today may not be the best fit next month, as fleet-level patterns shift.

• Continuously evaluates whether each workload is on the right GPU type and instance shape, within a given cloud
• Recommendations are grounded in the same multi-dimensional utilization data that drives fractioning
• Guardrail: lower spend is only recommended when performance is preserved or improved

• Extends the same comparative analysis across cloud providers, surfacing where a workload would run materially cheaper or faster elsewhere
• The output is signal grounded in measured utilization rather than list-price math; placement decisions remain the operator’s
• End result: feeds directly into the procurement and capacity-planning loop for multi-cloud and hybrid fleets

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