The companies spending the most on GPUs
have the least proof it's working.
If you run AI in production, GPUs are your biggest bill — and the 'optimizations' meant to shrink it are mostly unproven, sometimes quietly wrong. Kernalin makes your models faster and cheaper on the hardware you already own, and proves every gain is real. Here's the case, in plain terms.
Two expensive problems, hiding in plain sight.
Your models leave speed on the table.
Most AI workloads run well below what the hardware can do. On one real H100 kernel, Kernalin found a verified 3.48× speedup. Instead of using what they own, teams buy more GPUs to keep up — paying twice for performance they already have.
And the usual fix ships bugs at speed.
Letting an AI rewrite your model is the fast path — and a dangerous one. It's rewarded for looking faster, not being right, and a normal test misses most of the ways it breaks. You find out in production, if at all.
Faster models, proven correct. That's the whole product.
Kernalin proposes many ways to speed up your model, then runs each through a gauntlet that rejects anything wrong, dishonest, or only faster by luck. One proven winner comes out.
Illustrative counts. In our H100 run, 1 of 4 Claude rewrites was kept; 3 were correct but not faster.
- 01
Smoke test
queuedDoes the candidate run and produce output at all?
- 02
Shape sweep
queuedTall, wide, deep-K, vocab, and LLM shapes — not just the demo case.
- 03
Adversarial inputs
queuedDistribution-matched fuzzing and input transforms.
- 04
Determinism
queuedBitwise-identical output across repeated runs.
- 05
Edge cases
queuedNon-power-of-two and boundary shapes (1023, 4097, 50257).
- 06
Anti-cheat
queuedInput-differential + op-coverage: no dropped ops, no ignored inputs.
- 07
Statistical benchmark
queuedBayesian ROPE verdict over a bootstrap posterior vs. the noise floor.
Correct · not gaming the benchmark · genuinely faster
One number ships only if it's correct, honest, and genuinely faster. Everything else is stopped before it ever reaches you.
The moat is the part no one else has built.
Injected bug: max never subtracted from the exponent
A single demo-case probe
how most tools checkWith kv_len = 1, softmax over one element is exactly 1.0 — the output is v₀ regardless of the entire score path.
Kernalin · structural verification
how we checkA structural check sees the missing subtraction immediately — it doesn't need an input that happens to trigger the overflow.
What a verified 30% looks like on your P&L.
Illustrative. Actual savings depend on your workloads and hardware — we'll scope real numbers with you.
The window is open, and it won't stay that way.
GPU spend is exploding
AI compute is now the single largest infrastructure line for most AI companies — and it only grows as usage does.
Everyone optimizes. No one verifies
AI can rewrite a model to look faster in seconds. Almost nothing checks that it still produces the right answer.
The stakes just went up
As models move from demos to revenue, a silent quality regression isn’t a bug — it’s lost customers and lost trust.
The same number, for three rooms.
Founders & CEOs
Cut your largest infrastructure cost without betting your product quality on a benchmark you can’t verify.
Infra & platform leaders
Give your team optimization they can defend — in code review, in an incident, and in front of the board.
Investors
A durable, technical moat in the fastest-growing cost center in software. The verification is the defensibility.
Spend less on GPUs. Trust every number.
Tell us your GPU workloads and we'll scope the savings — with proof, not promises. Early access is open.