From 6 Hours to 2: Fixing the Memory Wall in Batch AI Pipelines
Why throwing more GPUs at batch inference doesn't work — and the three infrastructure optimizations that actually cut our pipeline runtime by 66%.
I build the infrastructure that takes AI from demo to production.
Currently, I'm architecting the orchestration layer at Fractal.ai, enabling autonomous agents to collaborate on complex workflows.
Previously, I built high-scale computer vision systems at Amazon to inspect millions of packages daily.
Agents are only as capable as the rails they run on.
The best systems are architected for the edge case, not the happy path.
Models are easy; making them work together reliably is hard.
Deployment is the beginning, not the end.
Why throwing more GPUs at batch inference doesn't work — and the three infrastructure optimizations that actually cut our pipeline runtime by 66%.
Academic benchmarks show 90%+ accuracy. Production systems hit 10-31%. The dangerous gap is semantic errors that look correct but aren't.
Vector RAG fails at multi-hop reasoning. GraphRAG fixes it — until entity resolution breaks, and errors compound exponentially across every query.
We migrated a B2B UI library from React to Svelte 5. The bundle dropped 60%, FCP improved 400ms, and the real trade-offs were not what we expected.
Explore advanced React patterns including compound components, render props, and custom hooks that help build maintainable and scalable applications.