Hacking the Inference Pareto Frontier - Kyle Kranen, NVIDIA
Aug 1, 2025 · 20:25
Kyle Kranen, architect of NVIDIA Dynamo, explains how to break the inference Pareto frontier by manipulating trade-offs between quality, latency, and cost using a toolkit of techniques. Disaggregation separates prefill and decode phases, achieving up to 2× tokens per second per GPU at fixed latency for LLaMA 70B on 16 H100s. Smart routing maximizes KV cache hits, asymptotically reducing prefill work as deployments scale. Structure from agentic workloads, like inference-time scaling, enables smaller models (e.g., 8B queried 3–4 times) to match larger models' quality at lower cost. KV manipulation offloads caches during tool calls (e.g., 30-second delays) to avoid re-prefill. Dynamism adjusts worker specialization and autoscales prefill/decode ratios in real time to handle shifting user distributions, ensuring disaggregation reaches maximum potential.
