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State of vLLM 2026 | Inferact | Ray Summit 2026
Accelerating Large Language Models: vLLM on TPUs | Google | Ray Summit 2026
AMD and vLLM: What's New | AMD | Ray Summit 2026
Serving a Frontier Open Model with Ray & vLLM | Nscale | Ray Summit 2026
GLM-5 RL Training with vLLM | Prime Intellect | Ray Summit 2026
Serving LLMs for Agents at Scale with Ray + vLLM | JPMorgan Chase | Ray Summit 2026
NVIDIA and vLLM Full-Stack Collaboration for DeepSeek and MiniMax Performance | Ray Summit 2026
vLLM and the State of AI Inference | Simon Mo (Inferact) | Ray Summit 2026
PyTorch loves vLLM | Meta | Ray Summit 2026
Optimizing vLLM for Intel CPUs and XPUs | Ray Summit 2024
vLLM in 2026: Challenges and Optimizations
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Last Updated: October 3, 2026
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In this session, we explored the latest updates in Agentic production traffic brings long multi-turn sessions, heavy prefix reuse, and bursty, heterogeneous requests that challenge ... Large language models demand significant computational power for inference, and TPUs are becoming a first-class target for ... A simpler setup delivered more tokens per second, with the first token arriving sooner for a single request, than the model card's ... Training trillion-parameter MoE models on long-horizon agentic tasks demands that inference and optimization scale ... As LLMs grow in size, context length, and architectural complexity,
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