Prefill Is Compute-Bound. Decode Is Memory-Bound. Why Your GPU Shouldn’t Do Both.

# Running AI Models More Efficiently: Why Splitting Up the Work Saves Money If you're running AI chatbots or language models, you're probably overspending on computing power. A new approach splits the work into two separate phases—one that needs raw processing speed and one that needs fast memory access—and runs them on different hardware instead of the same GPU, cutting costs by half to three-quarters. Most teams haven't switched yet, which means they're likely paying significantly more than they need to for the same results.
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. appeared first on Towards Data Science.
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