Lizard inference engineering: Compile shaders once
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Inference Engineering · Day 3 · Morning
A startup trace is easier to trust when one expensive step stops repeating.
Shader compilation is a good example. If the first launch is slow, that is expected on some GPU paths. The more useful question is whether later launches are still paying the same warm-up cost. When that happens, the slowdown is not really about inference speed. It is repeated setup work hiding in the launch path.
Both native engines now persist compiled GPU shaders, so the first warm-up is paid once instead of on every load. That changes how I read the trace. A cold start can still be expensive, but a warm start should converge. If it does not, I want to look at cache persistence, residency decisions, and any path that forces the runtime to rebuild GPU work before it reaches the model.
In practice I split startup into three buckets: cold load, warm load, and steady-state decode. Cold load tells me what the system pays to become ready. Warm load tells me whether setup work is being repeated. Steady-state decode is where model-path changes should show up clearly. If those three are mixed together, you end up tuning the wrong layer and the numbers stay noisy.
That is why this small persistence change matters. It makes startup behavior more legible. A trace that used to look like a slow model can now point to the real culprit: repeated shader compilation, repeated cache rebuilds, or a launch sequence that is doing more work than it should.
When you profile GPU startup, what is the first thing you separate: cold load, warm load, or decode?
Engineering fact: Both native engines persist compiled GPU shaders so the first warm-up is paid once instead of on every load.
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#GPUShaders #ShaderCompilation #StartupLatency #NativeRuntime #PerformanceDebugging
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