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Specialized networking for AI workloads

Build alternatives to TCP optimized for AI cluster traffic. Serve teams running large-scale distributed ML training.

Why now

New research shows TCP bottlenecks at AI scale, creating demand for alternatives.

Who for
ML engineers at scale
Business model
Enterprise licensing
Effort
A few months

AI training clusters have unique networking patterns that TCP handles poorly - small messages, many parallel connections, and tolerance for some loss.

Develop a UDP-based protocol with ML-specific congestion control and message batching. Package as a drop-in library for PyTorch/TensorFlow.

Sell to cloud providers and enterprises running large training jobs. Open source core with paid management tools.

MVP is a basic Python library that shows throughput improvements in benchmarks.

Risk is large players developing in-house solutions first.

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Specialized networking for AI workloads — Ideas