NVIDIA Dynamo is inference-serving software released alongside Blackwell Ultra that maximizes token throughput and minimizes response latency for AI factories — when combined with Milvus for retrieval, it reduces the total latency of RAG responses by optimizing the model serving side of the pipeline.
In a RAG system, latency has two components: retrieval time (Milvus) and generation time (LLM). Dynamo targets the generation component: it manages model request batching, KV cache sharing, and GPU memory allocation across concurrent inference requests to maximize throughput per Blackwell GPU. The result is lower average and P99 generation latency, which directly reduces the end-to-end time users experience waiting for RAG responses.
For self-hosted Milvus deployments where you also self-host your LLM (common with Llama 4 or Gemma 4), running Dynamo as the inference server on the same Blackwell cluster as your Milvus instance minimizes network latency between retrieval and generation. The retrieved vectors never leave the local GPU cluster, and Dynamo can intelligently schedule inference requests to overlap with Milvus retrieval operations.
The practical deployment guide: run Milvus on the CPU/storage tier of your Blackwell cluster for vector index operations, and run Dynamo on the GPU tier for LLM inference. Milvus’s NVLink-aware configurations can transfer embedding data directly to GPU memory for cuVS-accelerated search without a CPU round-trip.
Related Resources
- Milvus Performance Benchmarks — serving performance
- RAG with Milvus and vLLM — model serving integration
- Milvus Overview — deployment architecture