Red Hat AI(@RedHat_AI):Is your CPU:GPU ratio still built for training? In agentic workloads, 50-90% of end-to-end latency is CPU-side tool processing, not GPU math (Georgia Tech + Intel). That's pushing the CPU:GPU ratio from 1:8 in training toward 1:1, sometimes 4:1. And vLLM's CPU backend already runs PagedAttention, prefix caching, and continuous batching across x86, Arm, IBM Z, and experimental Apple Silicon. @_soyr_ and @__gracecaroline on where inference compute should live:

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2026.08.27 14:07

Is your CPU:GPU ratio still built for training? In agentic workloads, 50-90% of end-to-end latency is CPU-side tool processing, not GPU math (Georgia Tech + Intel). That's pushing the CPU:GPU ratio from 1:8 in training toward 1:1, sometimes 4:1. And vLLM's CPU backend already runs PagedAttention, prefix caching, and continuous batching across x86, Arm, IBM Z, and experimental Apple Silicon. @_soyr_ and @__gracecaroline on where inference compute should live:

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