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Researchers describe and demonstrate distributed inference techniques for running large language models across multiple servers or devices, including cache-aware routing and hardware-accelerated setups

Research publication Provisional 45% confidence first seen

IBM Research describes a distributed inference framework that coordinates clusters of model servers to handle concurrent LLM requests more efficiently, including prefix-aware routing to increase cache reuse and reduce redundant computation. Apple ML Research reports benchmarks using MLX with RDMA-over-Thunderbolt on Mac Studio clusters for running and scaling local and distributed LLM inference.

Distributed inference framework MLX llm-d Apple silicon M5 Mac Studio Muse Spark Open Prefix-aware routing RDMA-over-Thunderbolt Thunderbolt Unified Memory

Source coverage

How distributed inference keeps large AI models moving IBM Research Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio Apple Machine Learning Research

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