Cut your GPU bill
without breaking your models.
GPUs are the biggest line item in your AI budget — and most "speedups" are unproven or quietly wrong. Kernalin makes your models run faster on the hardware you already own, and proves every gain is real. Fewer GPUs for the same work, with outputs checked against your reference. A number you can put in front of your board.
- REFUTEDattn_v3.cu claims −51%max never subtracted · overflow hazard
- EQUIVALENTgemv_tiled.cu claims −6%within this GPU’s noise floor
- REFUTEDsoftmax_fast.py claims −44%ignores its input · reward-hack
- REFUTEDrope_apply.cu claims −39%wrong pairing · invisible at pos 0
- VERIFIEDflash_fwd.cu claims −32.7%correctness-clean · credibly faster
Your GPU budget leaks in two places at once.
If you run AI in production, GPUs are one of your biggest bills — and two silent problems keep it higher than it should be.
You pay for speed you never get
Most AI models run far below what your GPUs can actually do. That gap is real money — so teams buy more hardware to compensate instead of using what they already own.
And you can’t trust the fixes
The usual way to go faster is to let an AI rewrite your model. It often ships something subtly wrong — a quiet drop in quality you only discover in production, if at all.
Kernalin closes both — faster models, proven correct, on the fleet you already own.
Watch a single kernel travel the whole pipeline.
- Upload
- Profile
- Diagnose
- Optimize
- Verify
- Benchmark
- Ship
1 · Upload. Workload accepted and statically scanned — nothing is executed.
2 · Profile. Ranked by device time — occupancy, bandwidth, and tensor-core utilization captured where counters are allowed.
3 · Diagnose. The memory analyzer names the bottleneck — and the fix.
Fix: operator fusion + expandable_segments.
4 · Optimize. The AI copilot proposes a fused variant, grounded in the diagnosis.
@triton.jit # online softmax- m = tl.max(x, 0); e = tl.exp(x - m)+ m = tl.max(x, 0, keep_dims=True)+ e = tl.exp(x - m) # fused+ s = tl.sum(e, 0, keep_dims=True)Why: one pass instead of three — cuts a full DRAM round-trip.
5 · Verify. Five correctness stages + anti-cheat. Two faster-looking variants were rejected here.
- Correctnesspass
- Shape sweeppass
- Adversarialpass
- Determinismpass
- Edge casespass
- Anti-cheatpass
6 · Benchmark. A Bayesian ROPE verdict over the full latency distribution — not a single number.
7 · Ship. The CI gate passes — the verified winner merges. The number you saw is the number you keep.
Anyone can make a model faster.
Almost no one can prove it still works.
That gap is the whole company. Every proposed speedup runs a gauntlet of 7 checks — and only the ones that are correct, honest, and genuinely faster ever make it 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.
Let an AI speed up your model, and it will cheat to win.
It skips the work
Drops a step your model actually needs. Fast — and wrong.
It fakes the answer
Hands back a memorized result instead of computing one.
It games the test
Looks faster only on the one example you happened to measure.
You'd never know from the benchmark — it says "faster" either way. So most tools just ship the number. Kernalin refuses to, until it's proven real.
Typical AI optimizer
UnprovenA headline number with nothing behind it. If the kernel dropped an op, ignored an input, or memorized the reference output, you ship a bug at speed.
Kernalin
VerifiedEvery speedup survives correctness, anti-cheat, and statistical checks before it counts. The number you see is the number you can ship.
Most checks pass the bug.
We measured exactly which ones.
Ratios come from the engine's bug-class tables and the PTX fixtures that ship with it.
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.
The depth behind the guarantee.
Founders can stop here — the promise is simple: faster models, proven correct. What follows is for the people who'll pressure-test it: exactly how Kernalin profiles, optimizes, benchmarks, and gates every change.
One platform for the entire GPU optimization lifecycle.
See exactly where your GPU time is going.
Low L2 reuse on rmsnorm — recommend shared-memory tiling + operator fusion.
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True — reclaim 2.79 GB stranded in split blocks.
Seven ways VRAM wastes your money — found, with fixes.
An AI copilot built for GPU performance — not chat.
@triton.jit
def softmax(x_ptr, y_ptr, n):
- m = tl.max(x, 0)
- e = tl.exp(x - m)
+ m = tl.max(x, 0, keep_dims=True)
+ e = tl.exp(x - m) # fused, online
+ s = tl.sum(e, 0, keep_dims=True)
tl.store(y_ptr, e / s)Online softmax fuses the max/exp/sum reduction into one pass, cutting a full DRAM round-trip. Grounded in the profiler's memory-bound diagnosis.
99.4% of the posterior mass sits below the ROPE — convincingly beyond what this GPU's jitter could fake. This one earns the number.
Stop trusting single benchmark numbers.
Performance is only half the equation.
Illustrative UI. Figures are examples, not customer claims.
Your benchmark shouldn't be able to lie to you.
Optimization that keeps running on a schedule and on every PR.
Optimization Agent
Consumes profiling telemetry, generates optimization hypotheses, and scores confidence.
Benchmark Agent
Runs multiple workload variants, compares results, and selects the fastest implementation.
Regression Agent
Compares baseline metrics, detects anomalies, and triggers proactive notifications.
A pipeline engineered to be trusted.
From workload to verified speedup.
- 1
Upload workload
Drop in a Triton, PyTorch, CUDA, or ONNX workload. Kernalin statically checks it — nothing runs on intake.
- 2
Profile
Capture timing and op profiles (plus hardware counters where the host allows) into an immutable run history.
- 3
Identify bottleneck
The memory analyzer pinpoints fragmentation, uncoalesced access, cache misses, spills, and more.
- 4
Generate optimization
The AI copilot proposes kernel variants, grounded in the profiler diagnosis and a technique playbook.
- 5
Verify correctness
Every variant runs the 5-stage gate plus anti-cheat — reward-hacks are rejected here.
- 6
Benchmark
A Bayesian ROPE verdict decides credibly faster / equivalent / regressed against the noise floor.
- 7
Deploy / gate
Ship the verified winner, or block the regression in CI. The number you see is the number you keep.
Built for the people who own GPU performance.
CUDA & kernel engineers
Optimize faster without ever sacrificing correctness. An expert co-pilot that never ships a wrong "optimization."
ML / AI engineers
Improve inference latency and throughput without becoming a GPU-assembly expert.
Platform & infrastructure teams
Protect performance across the fleet with regression gates and continuous verification.
CTOs & engineering leaders
Control GPU spend and prove production performance with an auditable trail behind every number.
Built for teams betting real money on GPUs.
If GPUs are a seven-figure line on your P&L, "trust me, it's faster" isn't good enough. Kernalin turns optimization into something you can measure, verify, and defend — to your engineers, your board, and your investors alike.
Optimize with AI. Prove it with Kernalin.
Join the early-access program and bring verification-first optimization to your GPU workloads.