KAI
Ship faster CUDA. Automatically.
✓ TC-GNN connected · Blackwell ready · Autopilot online
Performance overview
Turn GPU bottlenecks into verified speedups.
RTX PRO 6000 BlackwellSM120 · 96 GB · connected
Your next optimizationREADY
Everything is connected
TC-GNN Tensor Core Training
KAI profiles the workload, tests the best ideas, and delivers the fastest verified implementation—without changing your workflow.
✓Connect
▥Analyze
✦Improve
◇Verify
Starting performance · epoch time0.00 msMeasured across four TC-GNN datasets
KAI Autopilot
A complete performance team, always working in the background.
Your workloadConnected
≡
Your success metricspeed · quality · guardrails
</>
Your CUDA projectreal code · real constraints
▧
Your target GPUarchitecture · compiler · device
✦
Your quality bartests · tolerances · rollback
KAI AutopilotOne goal. Every step coordinated.
Always on
KAI chooses the next best action from live evidence and measured results.
Finds the highest-impact opportunity
Builds hardware-aware candidates
Rejects regressions and proves each win
✦Autonomous decision-making · measured execution · built-in safeguards
Built-in actionsAlways on
□
Isolatesafe experiments
±
Improvetargeted changes
⬡
Buildproduction toolchain
✓
Protectquality gates
◷
Measurerepeatable results
▥
Understandprofiler intelligence
Run history
Candidate changes
Decision trail
Profiles + results
Impact report
🏆
Ready to shipProduction-ready winner
Performance intelligence
From profiler noise to the next best action.
KAI turns raw NCU data into a prioritized plan your team can trust.
Before KAIProfiler noise
Manual analysis
KAIIntelligencesignal to
action
action
With KAIPrioritized actions
Live intelligence
Finding opportunities
tcgnn_ncu_profile.json
Ingest
Normalize
Rank
Reason
Loading kernel context…veloq ncu
0signals read
0patterns matched
0opportunities
Highest-impact opportunitiesscanning…
◆
Recommended next actionShared-memory layout is holding performance backKAI will restructure memory access and verify the impact
+21.02%potential
Autopilot workflow
■Profile
➔
◆Plan
➔
✎Code
➔
✓Verify
ITERATION 1 / 4
Speedup
1.00×
Performance strategist
CUDA engineer
Quality guardian
Measured impact — lower epoch time is better.
TC-GNN
8.70 ms
PyG
8.89 ms
DGL
8.26 ms
KAI
2.48 ms
Geomean across amazon0505 / amazon0601 / artist / soc-BlogCatalog · RTX PRO 6000 Blackwell · 200 epochs
Production-ready · every quality gate passed
3.5×
faster. Fully autonomous.
TC-GNN · 8.70 ms → 2.48 ms · verified on Blackwell