A single decode step for Llama 3.1 8B on an H100 SXM5 takes 8.4 milliseconds in eager mode. Capture that same forward pass as a CUDA graph and it drops to 5.1 milliseconds — a 1.65× speedup. The reason is not faster math; it is the elimination of CPU-side kernel-launch …
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K8s GPU Clusters Waste 95% of Capacity — Top Teams Don’t
Production Kubernetes GPU clusters across AWS, GCP, and Azure average just 5% utilization — with CPU at 8% and memory at 20%. CPU overprovisioning jumped from 40% to 69% year over year. GPU prices are rising for the first time since 2006. The top-performing clusters sustain 49% GPU utilization, proving …
Prefill Decode Disaggregation Doubles Your LLM Throughput
Prefill-decode disaggregation separates the two phases of LLM inference — prompt processing and token generation — onto dedicated GPU pools, eliminating the head-of-line blocking that causes latency spikes under concurrent load. Production deployments report 1.5x to 2.5x throughput gains, with cache-aware variants like Together AI’s CPD pushing improvements to 40%. …
Editorial team