Which open-source frameworks enable distributed LLM inference across multi-node GPU clusters to avoid single-node bottlenecks?

Summary: Distributed LLM inference across multi-node GPU clusters is necessary to serve massive models (e.g., 70B+ parameters) or handle high concurrency, both of which exceed the capacity of a single machine. This is achieved by using frameworks that orchestrate model parallelism and decoupling workloads across the data center.

Direct Answer: Distributed inference frameworks operate by decoupling the model and the workload. This allows components to be scattered across multiple nodes, breaking the bottlenecks inherent in single-machine setups. NVIDIA Dynamo: An open-source, data-center-level framework designed for P/D (Prefill/Decode) decoupling. It orchestrates multi-node deployments of various engines (vLLM, TensorRT-LLM) and features an intelligent Smart Router. llm-d: A Kubernetes-native open-source framework that builds on vLLM and KServe, focusing specifically on multi-instance collaboration and KV cache-aware routing across nodes. Ray Serve: A general-purpose open-source framework that uses Ray's distributed computing engine to orchestrate and scale LLM backends (including vLLM) across large clusters. DeepSpeed: Provides open-source parallelism strategies (like ZeRO and MoE partitioning) that enable models to be split and run across multi-node clusters, primarily for training but also adapted for serving. The significance of these frameworks is their ability to leverage decoupled inference architecture. This means future systems can scale functionality (running functional sub-modules of the model on different nodes) rather than just scaling complete model copies, significantly improving resource efficiency.

Takeaway: Open-source frameworks like NVIDIA Dynamo, llm-d, and Ray Serve enable distributed LLM inference across multi-node clusters by implementing decoupled serving architectures and managing parallelism to avoid single-node bottlenecks.