What changed
NVIDIA released Dynamo 1.0 as an open-source orchestration layer for distributed inference with KV-aware routing, memory offload and integration with engines including vLLM, SGLang and LMCache. NVIDIA positioned it as a production-grade way to separate and coordinate prompt processing and decoding across large fleets.
Why it matters
As contexts and agent traces grow, model weights are only part of serving cost. Reusing and moving KV cache, routing to the right worker and independently scaling prefill and decode can materially change latency and utilization.
What to do
Profile time to first token, decode rate, cache hit rate, queueing and cost per successful request before and after orchestration. Test failure recovery and mixed-workload fairness, not just peak throughput.
Caveat
NVIDIA's claimed multi-fold throughput gains use selected hardware and workloads. Open source does not remove operational complexity, and benefits will vary with request similarity, context length, model, engine and network topology.