Inference, built around your traffic
Your real request mix, not a generic benchmark, decides kernels, engine, batching, quantization, and the card.
A dedicated endpoint built around your traffic and a model trained on your data. Both keep getting better after they ship, run by a system that improves with every customer.
Bring the model, a sample of real traffic, and a target. We build the endpoint around them, train the model on your data, and keep improving both after they go live.
Your real request mix, not a generic benchmark, decides kernels, engine, batching, quantization, and the card.
Post-train an open model on your data and your outcomes. The weights are yours, and they serve on the same endpoint.
Every candidate, a config or a checkpoint, has to hold correctness and your SLO on held-out traffic before it replaces what runs.
Traffic drifts, models update, new cards land. The system measures again and adapts, and only verified gains ship.
The fastest setup is never a single switch. Kernels, serving engine, batching, quantization, hardware, traffic shape, and the weights themselves all push on each other.
Profile the stack on your real traffic to see where latency or throughput is lost: scheduling, decoding, kernels, memory pressure, or hardware fit.
Generate candidates across batching, decoding, quantization, engines, kernels, hardware, and training recipes, and measure every one of them.
Promote the fastest verified candidate, then keep profiling production traffic as load, models, and hardware change. Every solved endpoint shortens the next search.