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Scaling LLM Inference on TPUs: Recent Developements, Optimizations, and RL Integrations
Scaling Production LLM Inference Using EKS Auto Mode & Ray Serve | Ray Summit 2025
Scaling Massive Transformer Training with KubeRay | Capital One | Ray Summit 2026
vLLM and the State of AI Inference | Simon Mo (Inferact) | Ray Summit 2026
Serving Fast and Smart: Getting the Most Out of Ray Serve LLM on GKE | Google | Ray Summit 2026
Ray Summit 2026: 2-minute Recap
PyTorch loves vLLM | Meta | Ray Summit 2026
Ray Summit 2025 - Scaling Batch Inference and RL
Vertical Mobility: Inference from MVP to Trillion Parameters | CoreWeave | Ray Summit 2026
LLM Post-Training and High-Performance Serving | Anyscale | Ray Summit 2026
Tailor-Made Inference at Scale | Simplismart | Ray Summit 2026
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Last Updated: October 3, 2026
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Summary
Large language models demand significant computational power for Brittany Rockwell, Kyuyeun Kim, and Wenxin Dong from When infrastructure is siloed, I/O overhead can stretch development cycles into days. Capital One processed 3.5TB of data and ... "vLLM now supports over 1000 accelerator types and more than 5000 model architectures, and the PyTorch and vLLM are deeply intertwined, and the integration keeps getting stronger. At Watch Yi Sheng Ong and Eric Higgins, Software Engineers at Applied talk at "Github repository: github.com/anyscale/ Every use case needs different latency, cost, and compliance tradeoffs, but most teams either over-engineer one-off stacks or ...
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