Kernel Fusion and Inference Kernels: Squeezing Latency Out of GPUs

Inference latency often comes from kernel overhead, not just model size.

Kernel fusion reduces memory transfers and improves throughput.

Layer norm, activation functions, and linear layers benefit from fusion.

Attention kernels are especially sensitive to memory bandwidth and fusion choices.

Custom kernels can unlock major speedups but increase maintenance cost.

Use them when performance constraints justify the engineering effort.

Use profiling tools to identify kernel hotspots and memory stalls.

Optimize the heaviest paths first for best ROI.

Kernel optimizations may depend on specific GPU architectures.

Maintain fallbacks for older hardware and cloud instances.

Track kernel-level speedups against real workloads, not just microbenchmarks.

Rollback to stable kernels if a new fusion introduces numerical drift or instability.

Profile across GPU generations to ensure optimizations do not regress on older hardware.

Maintain a kernel compatibility matrix so deployments stay predictable.

Use automated regression tests to catch kernel performance drops early.

Benchmark kernel changes in production-like environments to avoid lab-only gains.

Log numerical error checks alongside performance metrics to prevent silent accuracy loss.

Capture kernel launch overhead metrics to spot hidden regressions.

Validate memory usage patterns to avoid unexpected OOMs after kernel changes.

Keep a kernel change log so regressions can be traced quickly.

Profile end-to-end latency to ensure kernel gains translate to user impact.

Bundle kernels with versioned drivers so rollbacks are consistent.

Document known kernel limitations so teams avoid unsupported configurations.

Align kernel benchmarking with real batch sizes so results reflect production traffic.

Automate kernel selection based on hardware detection to avoid fragile manual configs.

Keep a small canary fleet so new kernels prove stability before full rollout.

Does fusion change model outputs? No, if implemented correctly.

Is it worth it for small models? Less so; benefits scale with model size.

What is the biggest risk? Kernel bugs that are hard to detect in QA.