Production AI Architectures: Nine industry use cases

Larger models and larger datasets no longer fit on a single GPU node. Training and inference aren't the only workloads that need to go distributed; data processing itself now requires GPUs for video, documents, embeddings, and synthetic data. GPUs power the entire AI pipeline, and platform teams need a unified way to run it.

What’s inside this edition:

This ebook collects nine reference architectures from teams running AI in production at scale.

• Torc Robotics — Multimodal data processing at scale for autonomous trucking

• Coinbase — Distributed LLM inference for agent platforms

• BMW — Speech agents for real-time connected car

• Attentive — Large-scale model training for advanced recommendation systems

• Agreena — 10,000x faster satellite imagery processing for agriculture analytics

• Recursion Pharmaceuticals — Large-scale biological inference for drug discovery

• Runway — Foundation model training for video generation

• xAI — Scalable multimodal data processing for frontier model training

• Riot Games — Distributed reinforcement learning for fair gaming