Cutting decode latency on a production LLM serving stack
An illustrative walkthrough of how a serving team could use Kernalin to find, optimize, and verify a memory-bound decode kernel.
This is a demonstration case study using illustrative data. It shows the shape of a Kernalin engagement, not a real customer or real numbers.
Problem
A team serving a large language model sees decode-phase latency dominating tail response times. Profiling points at a handful of normalization and RoPE kernels, but every attempted hand-optimization risks subtle numerical drift that only shows up under production shapes.
Infrastructure
- PyTorch serving stack with custom Triton kernels
- RTX 4090 and H100 nodes
- Batched decode with variable sequence lengths
Baseline
Kernalin profiles the decode path and captures the run into immutable history. The rmsnorm kernel is flagged memory-bound with an L2 hit rate of 22% — a clear cache-locality problem, not a compute one.
Optimization
The AI copilot proposes fusing the normalization and scaling into a single pass, grounded in the profiler’s memory-bound diagnosis. It generates several Triton variants and benchmarks each.
Verification
Every variant runs the five-stage correctness gate plus input-differential and operation-coverage anti-cheat. Two variants that looked faster are rejected — one drifts on non-power-of-two sequence lengths, the other fails determinism. The surviving variant passes all stages.
Result
The verified variant lands a 1.49× speedup (−32.7% latency) on the decode kernel, with a Bayesian ROPE verdict of credibly faster and zero correctness regressions. The change ships through the CI gate with an auditable metric-diff on the pull request.