01 · The Engine
A loop runner you can deploy.
Generate → compile → benchmark → verify, packaged as a single engine that runs continuously over every priority workload, not as a one-off research script.
Continual Learning Infrastructure
Loop.
Turning silicon into production AI performance through continuous learning.
Kernels · Compilers · RuntimesBuilt by Activeloop
Fig. 01, Loop
Performance still depends on
scarce kernel experts.
Every chip generation resets the optimization clock. Each one needs hand-tuned kernels by a small bench of specialists, long before software catches up to silicon.
Constraint · 01
Each chip introduces constraints
New architectures require repeated optimization before software reaches peak performance.
Constraint · 02
Expert kernel engineering does not scale
Manual CUDA/PTX work depends on scarce specialists and slow iteration.
Constraint · 03
Abstraction split is unresolved
Standard libraries miss custom ops, fused workloads, and unusual shapes.
Constraint · 04
Production integration is incomplete
Generated kernels remain hard to deploy, monitor, and maintain in real systems.
Activeloop makes intelligence compound.
Loop runs a closed cycle over each priority workload: every generated kernel becomes feedback for the next, and every win raises the floor for everything that follows.
Input
Model + Hardware
Model graph
Operator traces
Hardware profile
Benchmark targets
Correctness tests
Fig. 03, Active Factory Loop
Output
Optimized Kernels
Optimized kernels
Performance reports
Reusable kernel library
Deployment hooks
Generate → Deploy → Learn. Every cycle, sharper.
01
GenerateCandidate kernels proposed from spec.
02
CompileLowered to the target backend.
03
BenchmarkRun on the target hardware profile.
04
VerifyCorrectness against the reference op.
05
RegressionsGuardrails against silent slowdowns.
06
DeployShipped behind deployment hooks.
07
LearnWins folded back into the next round.
Three modules sit under the loop.
An engine that runs the cycle, telemetry that keeps it grounded in real workloads, and a spec sheet that turns 'faster' into a number you can audit.
02 · Continuous Telemetry
Real traffic, not just micro-benchmarks.
Always-on signal collection from production. Every request, every regression, every win folds back into the loop, so the system learns from how the workload is actually used, not just how it was specced.
03 · Performance Spec
Measurable contract, signed up front.
Speedup targets, latency budgets, accuracy floors, written into the sprint contract before any code runs and audited at the end. No demos that don't survive production.
Performance is a story you can read on the chart.
Every claim in Loop has a measurement behind it. Step through the receipts: token throughput, kernel runs, autoresearch progress, and the partnership track record.
Autoresearch DeepLake Progress
527 experiments · 17 running-best improvements
Activeloop in production.
Loop is not a research demo. It builds on years of shipped engineering: open-source adoption, F500 production workloads, peer-reviewed research, and co-developed education.
"Physical AI, powered by vision-language-action models (VLAs), enables robots not only to see, but to perceive and reason. Activeloop and Pinkbot achieved 9x faster throughput with Intel Core Ultra Series 3."Source: Intel on X
"In science, sometimes you have to rethink the basics to make progress. Flagship's work with Activeloop has been all about that, getting back to the core of how we store and retrieve data for AI to speed up how we solve really tough scientific problems."
Mark Kim, Flagship Pioneering
Source: Flagship Pioneering case studyFaster time-to-performance.
Less dependence on scarce experts.
Book a demo to review Loop against 3-5 priority workloads. If we hit the agreed speedup targets, convert into a platform partnership.
- Duration
- 90 days
- Scope
- 3-5 priority workloads
- Trigger
- Speedup targets hit, then platform partnership