Systems

A serving system that groups multiple requests or decode steps together so accelerators stay busier, raising throughput while often adding wait time for each individual request.

A serving system that keeps an active decode set refilled as requests finish or become compatible, so accelerators spend less time waiting for a whole batch to turn over.

A serving system practice that turns a trained model plus its runtime stack into a live, versioned inference service on real hardware.

A serving system that waits briefly for compatible queued requests, then runs them together so accelerators stay busier while trading a small queueing delay for higher throughput.

A serving system that overlaps expert routing, communication, and compute so sparse MoE execution does not stall on movement alone.

The serving runtime layer that takes model weights plus live requests and turns them into token outputs by coordinating kernels, scheduling, memory movement, and cache state on real hardware.

A serving system concern that covers the live state the runtime must keep or move while answering requests, including model weights, KV cache growth, allocator overhead, and memory bandwidth limits.

A serving system that spills part of the KV cache to disk so very long sessions remain workable without keeping every token state in expensive fast memory.

A serving system that decides which queued request work runs next so operators can balance latency, fairness, throughput, and memory pressure.

A serving system that decides which model, hardware tier, or execution path should handle a request so operators can balance cost, latency, safety, and specialization.

A serving technique that uses a cheaper draft path to propose several next tokens, then asks the main model to verify them so decode-heavy requests can finish with fewer expensive turns.