Why across-the-stack optimization matters
The fastest setupis rarelya single switch.
Kernel choices, serving-engine behavior, batching, quantization, hardware, and traffic shape all push on each other — that is the OCRR Optimization Loop and the OCRR Optimization Engine.
Ocrr treats inference as a coupled system, not a menu of toggles. We model how each variable ripples through throughput, latency, and cost, then search the joint space for the configuration your workload actually rewards.
What the engine tunes
Kernels
GEMM and attention variants ranked per shape and precision, not one global pick.
Serving engine
Scheduling and prefill/decode behavior matched to your measured request mix.
Batching
Dynamic and continuous schedules tuned to your live request arrival curve.
Quantization
Per-layer precision explored against a quality floor you define up front.
Hardware
Accelerator choice, placement, and topology fitted to the workload profile.
Traffic shape
Steady, bursty, or long-context — the config re-tunes as the curve moves.
Measure, model,
then move the
whole stack.
Profile the workload
Replay realistic traffic to capture token length, concurrency, and arrival bursts.
Search the joint space
A surrogate model prunes the kernel-by-batch-by-precision combinatorial explosion.
Ship the config
The winning setup deploys as a versioned recipe your team can diff and roll back.
Re-measure, loop back
Live telemetry feeds the next pass as traffic and hardware drift.