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Transforming Ethernet from Underdog to Champion for AI Inference

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Modern AI inference is increasingly constrained by KV cache movement across prefill, decode, and storage tiers rather than GPU compute. This session demonstrates how a lightweight software layer enables RDMA-class performance on standard Ethernet networks without application changes, supporting high-performance disaggregated vLLM inference. Live benchmarks highlight improvements in time-to-first-token (TTFT) and inter-token latency (ITL).

July 21, 2026 16:50 - 17:10

Balaji Prabhakar
Balaji Prabhakar

VMware Founders Professor of Computer Science | Stanford University

Topic

AI Training & Inference

Session Type

Tech Talk

Related Product

Instinct, ROCm

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