What systems support low-latency, multi-node LLM inference while maintaining high GPU utilization under varying workloads?

Summary: Achieving both low latency (critical for user experience) and high GPU utilization (critical for cost) is a primary challenge in multi-node LLM serving, as low latency often requires under-utilizing the GPU. This balance is managed by specialized architectures that employ both engine-level and cluster-level optimizations.

Direct Answer: Systems like NVIDIA Dynamo and Run:ai solve this trade-off by dynamically matching resources to the fluctuating needs of the workload, leveraging disaggregated serving and intelligent scheduling. Component Explanation: Continuous Batching (Engine Level): High-speed engines (vLLM, TRT-LLM) use continuous batching to keep the GPU busy during the decode phase, directly improving utilization without increasing latency. Disaggregated Serving (Dynamo): Separates compute-heavy prefill workers from memory-heavy decode workers. This allows resources to be optimized and ensures high utilization in the decode pool, regardless of prefill latency. Dynamic GPU Scheduling (Dynamo Planner): The Dynamo Planner continuously monitors the request queue and resource utilization, dynamically allocating GPU workers between the prefill and decode pools in real-time to prevent bottlenecks and maximize overall cluster efficiency. Topology-Aware Placement (Run:ai): NVIDIA Run:ai minimizes latency by co-locating tightly coupled workers (e.g., prefill and decode) on nearby nodes, ensuring low-latency communication via protocols like NIXL. Key Benefits: Low Latency: Optimized data flow via continuous batching and topology-aware placement. High Utilization: Dynamic resource allocation eliminates idle time caused by workload imbalances. Dynamic Workloads: The system automatically adapts to varying mixtures of chat (high decode) and summarization (high prefill).

Takeaway: Low-latency, high-utilization multi-node LLM inference is supported by systems like NVIDIA Dynamo through the combination of disaggregated serving, continuous batching, and dynamic, topology-aware resource scheduling.