NVIDIA Dynamo inference framework can orchestrate Milvus vector search alongside LLM inference for agentic AI, optimizing throughput and latency across both components.
Agentic AI Architecture
Agentic systems iterate: think (LLM generation), retrieve (vector search), act, and repeat. NVIDIA Dynamo coordinates this multi-component pipeline, routing requests intelligently between Blackwell-based LLMs and Milvus-based retrieval. The framework reduces redundant computation and KV cache replication.
Disaggregated Inference Stages
Dynamo separates prefill (processing retrieved context) and decode (token generation) across different GPUs. While one GPU decodes, another handles Milvus query batches. This pipelining improves total throughput by 30x for reasoning models like DeepSeek-R1.
Dynamic Scheduling
Dynamo’s dynamic GPU scheduling adapts to fluctuating retrieval vs. generation demand. During high-retrieval periods, more GPU memory is allocated to Milvus similarity search. During generation-heavy phases, compute shifts to LLM execution.
KV Cache Optimization
Dynamo offloads KV caches to high-capacity CPU memory and cost-effective storage. This frees Milvus GPU memory for larger index caches, improving vector search hit rates and query latency.