Prefill Is Compute-Bound. Decode Is Memory-Bound. Why Your GPU Shouldn’t Do Both.
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Prefill Is Compute-Bound. Decode Is Memory-Bound. Why Your GPU Shouldn’t Do Both.
Inside disaggregated LLM inference — the architecture shift behind 2-4x cost reduction that most ML teams haven't adopted yet. The post Prefill Is Compute-Bound. Decode Is Memory-Bound. Why Your GPU Shouldn’t Do Both....
Editorial Analysis
I've been running LLM inference at scale for three years, and disaggregation is the operational shift nobody's talking about at conferences. The insight here cuts straight to hardware economics: prefill operations are compute-hungry (matrix multiplications on full sequences), while decode is pure memory bandwidth saturation (generating one token at a time). Running both on the same GPU means idling expensive compute during decode or wasting memory bandwidth during prefill. Teams I've consulted with typically consolidate prefill requests across batches on smaller, cheaper instances, then route decode to memory-optimized hardware. This maps cleanly to the modern data stack pattern we already use for OLAP versus OLTP separation. The 2-4x cost reduction isn't theoretical—it's achievable by rightsizing instance types to actual workload characteristics. If you're still running monolithic inference clusters, you're essentially paying for two machines' worth of capacity but using one machine's efficiency. Start instrumenting your prefill versus decode ratios now; that data becomes your disaggregation business case.