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Product overview

Make your AI faster and cheaper
— without ever shipping a worse model.

One platform for the whole job: find where your GPUs are wasted, make your models faster, prove every gain is real, and stop any regression before it ships. Verification runs through all of it, so speed never comes at the cost of accuracy.

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01 Available

Intake

Securely accept and statically check Triton, PyTorch, CUDA, and ONNX workloads.

02 Available

Profile

Capture timing and op profiles; occupancy, throughput and tensor-core use where counters are allowed.

03 Available

Optimize

Generate verification-gated kernel rewrites with the AI copilot (Claude).

04 Available

Verify

Correctness gate, anti-cheat, and a statistical verdict on every candidate.

05 Available

Benchmark

Statistically honest, regression-aware comparison across runtimes and GPUs.

06 Available

Gate & report

Block regressions in CI with an auditable metric-diff.

Every module

A complete, interconnected platform.

Twelve capabilities that share one contracts-first engine — so the editor, the CLI, and CI never disagree about what a kernel is or whether it's correct.

Workspaces

Projects, teams, and roles for organized optimization campaigns.
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Workload intake

Upload & static checks of Triton, PyTorch, CUDA, and ONNX files — nothing is executed on intake.
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Profiling

Timing, op profiles and memory analysis as immutable run history; hardware counters where the host allows them.
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AI optimization

Claude-generated kernel rewrites, each gated by correctness, anti-cheat and a statistical verdict.
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Benchmarking

Cross-runtime comparison (PyTorch, torch.compile, ONNX Runtime, TensorRT) with Bayesian ROPE verdicts.
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Verification

Correctness gate, anti-cheat, and statistical verdict — the foundation, not an add-on.
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Cost intelligence

GPU utilization, idle cost, and cost-per-token analytics with savings insights.
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Observability

Live GPU dashboards and kernel heatmaps.
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CI/CD

A GitHub Action gate that blocks regressions on every pull request.
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Alerts

Regression and cost-spike notifications to Slack, Microsoft Teams, and email.
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Autonomous agents

Optimization, benchmark, and regression agents working as one pipeline, on schedule and on PR.
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Enterprise

Role-based access, SSO over OIDC or SAML 2.0, and an audit log.
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See it in context

Profiling that turns telemetry into a plan.

Rank kernels by device time, read occupancy and bandwidth at a glance (where the GPU host allows hardware counters), and get a named bottleneck with a concrete fix — not a wall of counters.
Profiling Optimization
profiling · llama-decode · session #204 ● live
Kernel timeline ranked by device time
flash_attn_fwd compute 1.42 ms
rmsnorm memory 0.68 ms
rope_apply memory 0.41 ms
silu_mul memory 0.33 ms
kv_gather latency 0.21 ms
SM occupancy
78%
DRAM throughput
84%
Tensor-core util
61%
L2 hit rate
42%
Bottleneck: memory-bound

Low L2 reuse on rmsnorm — recommend shared-memory tiling + operator fusion.

benchmark · rmsnorm · RTX 4090 · batch 32
Baseline Candidate
Credibly faster
Baseline median
2.84 ms
Candidate median
1.91 ms
Speedup (ROPE)
1.49×
P(faster)
0.994

Bayesian ROPE verdict over a bootstrap posterior of the median-latency ratio — credibly faster, not "1% better and hoping the noise agrees."

Proof, not promises

Benchmarks with a verdict you can gate on.

Kernalin measures a full latency distribution and returns a Bayesian ROPE verdict against your GPU's noise floor. The number you see is the number you can ship.
How benchmarking works
How it works

From workload to verified speedup.

  1. 1

    Upload workload

    Drop in a Triton, PyTorch, CUDA, or ONNX workload. Kernalin statically checks it — nothing runs on intake.

  2. 2

    Profile

    Capture timing and op profiles (plus hardware counters where the host allows) into an immutable run history.

  3. 3

    Identify bottleneck

    The memory analyzer pinpoints fragmentation, uncoalesced access, cache misses, spills, and more.

  4. 4

    Generate optimization

    The AI copilot proposes kernel variants, grounded in the profiler diagnosis and a technique playbook.

  5. 5

    Verify correctness

    Every variant runs the 5-stage gate plus anti-cheat — reward-hacks are rejected here.

  6. 6

    Benchmark

    A Bayesian ROPE verdict decides credibly faster / equivalent / regressed against the noise floor.

  7. 7

    Deploy / gate

    Ship the verified winner, or block the regression in CI. The number you see is the number you keep.

Under the hood

A pipeline engineered to be trusted.

Contracts-first, sandboxed, and anti-reward-hacking by construction.
01
Intake
Accept & statically scan
02
Verify
5-stage correctness gate
03
Measure
Controlled timing
04
Decide
Bayesian ROPE verdict
05
Anti-cheat
Input-differential + op-coverage
06
Optimize
LLM Coder / Judge loop
07
Gate
Tiered CI regression gate
08
Report
Auditable run-log
Contracts-first architecture Sandboxed, network-isolated execution Anti-reward-hacking by construction Immutable audit trails RL-ready trajectory logging Proven end to end on an H100
Explore the architecture
Early access is open

Optimize with AI. Prove it with Kernalin.

Join the early-access program and bring verification-first optimization to your GPU workloads.

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