Lizard inference engineering: Decode is a memory-bandwidth problem

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Inference Engineering · Day 4 · Morning

Peak FLOPS is a poor shorthand for decode performance on local LLMs.

Autoregressive generation reads the model weights again for every token. That means the cost you feel first is often bytes moved, not raw arithmetic capacity. On consumer hardware, memory bandwidth can become the ceiling before the compute units are busy enough to matter.

That shifts the tuning order. If decode is the bottleneck, the practical questions are about weight traffic, quantization, layout, and cache behavior. A smaller quantization can help twice: fewer bytes to move and less pressure on the memory subsystem. In that case, adding threads or chasing a bigger peak FLOPS number may change less than expected.

For a local runtime, this is the difference between a model that looks large on paper and one that actually keeps up during generation. It also explains why decode profiling needs a bytes-per-token view, not just a “compute utilization” view.

When you profile local decode, what tends to move the needle more for you: quantization choice, KV-cache behavior, or memory layout?

Engineering fact: Autoregressive decode repeatedly reads model weights, so bytes moved per token often matter more than peak arithmetic throughput.

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#llm #decode #memorybandwidth #quantization #localai

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