Use cases Verified optimization,
Verified optimization,
applied.
Kernalin fits wherever a GPU kernel decides your latency or your bill. Every workload below runs the same verification gauntlet — a speedup only counts once it's proven correct.
By workload
One engine, every hot path.
From decode loops to GEMM tiles, the pattern is the same: profile the bottleneck, generate a
candidate, and keep it only if it's correctness-clean, anti-cheat-clean, and credibly faster.
By team
Whoever owns the GPU, Kernalin has your back.
The same verified-optimization loop reads differently depending on where you sit — engineer,
ML practitioner, platform owner, or the leader signing the cloud invoice.
faster, safely
CUDA & kernel engineers
Optimize faster without ever sacrificing correctness. An expert co-pilot that never ships a wrong "optimization."
better inference
ML / AI engineers
Improve inference latency and throughput without becoming a GPU-assembly expert.
fleet-wide guardrails
Platform & infrastructure teams
Protect performance across the fleet with regression gates and continuous verification.
provable ROI
CTOs & engineering leaders
Control GPU spend and prove production performance with an auditable trail behind every number.
The common thread
A faster kernel isn't useful if it's wrong.
Every use case above shares one guarantee. Kernalin never reports a speedup it hasn't
proven — no dropped operations, no memorized outputs, no benchmark that lies to you.
That's what makes an optimization safe to ship across all of them.
Correctness gate
Five stages, every candidate — smoke, shapes, adversarial, determinism, edges.
Anti-cheat
Input-differential and operation-coverage checks close the reward-hack gap.
Honest benchmark
A Bayesian ROPE verdict against your GPU noise floor — not a naive 1% gate.
Early access is open
Find your workload. Prove the speedup.
Bring verification-first optimization to the kernels that decide your latency and your GPU bill.