Lizard inference engineering: Compile the execution plan
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Inference Engineering · Day 3 · Evening
If you trace token latency in a local runtime, the obvious place to look is often the wrong one.
The model kernels can look healthy while the loop still loses time to coordination: CPU work sets up the next step, the GPU waits for a handoff, and the runtime repeats the same planning work for every token. That kind of overhead is small in isolation, but over a long decode it becomes part of the profile you actually feel.
Caterpillar changes that by rebuilding the execution core around a pre-compiled compute-graph plan. The useful effect is not just that the graph exists. It is that the runtime can stop reconstructing the same execution shape on every token and instead follow a plan that is already laid out. That reduces per-token CPU
GPU synchronization, which is exactly the sort of cost that hides between kernel launches.The practical lesson is to separate math cost from orchestration cost. When I look at a slow generation path, I want to know whether the runtime is paying for repeated scheduling, repeated graph setup, or repeated handoff boundaries that could have been planned once. If the execution shape is stable, the hot path gets simpler. If it is not, even good kernels can end up carrying latency that never shows up in FLOPS.
One thing I have found useful is to inspect the decode path at the token boundary, not just the kernel level. If the coordination layer is doing more work than expected, the fix is usually structural rather than numerical.
Have you seen a case where reducing CPU
GPU synchronization moved token latency more than changing the kernel itself?Engineering fact: Caterpillar rebuilds the execution core around a pre-compiled compute-graph plan to reduce per-token CPU
GPU synchronization.Lizard The AI Runtime You'll Own—Not Rent.
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#Caterpillar #ComputeGraph #Inference #WindowsAI #LocalLLM
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