A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network

In-network computing techniques, exemplified by NVLink SHARP (NVLS), offer a promising approach to addressing the communication bottlenecks in LLM inference by offloading collective operations such as All-Reduce to switches. However, the accelerator-centric architecture of NVLS suffers from two fundamental limitations: 1) it relies on GPU load instructions to trigger in-switch reduction, which means that the data reduced in the switch must be transferred back to the initiating GPU rather than being broadcast directly, thereby introducing unnecessary communication overhead; 2) due to its architectural constraints, NVLS cannot offload operators that are not decomposable into memory-semantic instructions, such as the in-network quantization (INQ) proposed in this work. As a result, All-Reduce in NVLS during inference still operates at 16-bit precision, leading to substantial bandwidth waste. To address these limitations, we propose SCIN, the first switch-centric in-network architecture for multi-accelerator shared-memory networks, enabling both low-latency and high-bandwidth All-Reduce. Specifically, we introduce an in-switch accelerator (ISA) capable of directly accessing the memory regions in attached accelerators for in-network processing, together with a co-designed communication fabric that enables such access with negligible protocol overhead. SCIN delivers lower All-Reduce latency than NVLS by eliminating redundant data movement. Moreover, SCIN enables INQ for All-Reduce, reducing its precision to 8 bits and nearly doubling bandwidth with negligible accuracy loss. We also present a multi-FPGA prototype of SCIN to validate its feasibility and effectiveness. Simulation results for an 8-GPU system show that our design accelerates All-Reduce by up to 8.7x for small messages and 3.8x for large messages, yielding up to 1.74x TTFT speedup and 1.34x TPOT speedup on LLaMA-2 models.