IBM Research Explains How Distributed Inference Coordinates Large AI Models

Serving large language models at scale involves more than just running a model on a GPU; it requires coordinating thousands or millions of requests with varying priorities and context lengths. IBM Research explains this challenge using an air traffic control analogy: just as controllers manage aircraft location, destination, and fuel needs, distributed inference frameworks must optimize accelerator usage without creating long queues or redundant computations.

The core of this coordination lies in working above individual model servers like vLLM or SGLang. A distributed inference framework orchestrates groups of servers across a cluster, considering queue depth, cache availability, and server roles to route requests effectively. This layer becomes increasingly necessary as AI agents introduce irregular traffic patterns, such as bursts of related requests followed by pauses for tool calls.

One critical innovation is prefix-cache-aware routing. When a model processes a prompt, it stores intermediate states in a KV cache. If subsequent requests share the same beginning text—common in chatbots with fixed system instructions or coding assistants examining the same repository—the model can reuse this stored computation. Conventional load balancers might send these requests to any available server, forcing redundant processing. Prefix-aware routing instead directs requests to servers that already hold the relevant context, reducing latency and increasing throughput.

To support this reuse, KV-cache offloading extends the model's working memory. Accelerator memory is fast but limited, often filled by long prompts and concurrent sessions. Offloading moves less immediately useful cache entries to slower but larger tiers like CPU memory or local storage. While retrieval from these tiers is slower than from accelerator memory, it is significantly faster and cheaper than recomputing the entire prompt, thereby expanding effective cache capacity.