Introduction to State Of Vllm 2026 Inferact Ray Summit 2026
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State of vLLM 2025 | Ray Summit 2025
AMD and vLLM: What's New | AMD | Ray Summit 2026
PyTorch loves vLLM | Meta | Ray Summit 2026
Serving vLLM on Intel GPUs, CPUs, and Gaudi | Intel | Ray Summit 2026
GLM-5 RL Training with vLLM | Prime Intellect | Ray Summit 2026
🎙️Simon Mo CEO of @inferact: 5 new VLLM features in 2026!
Scaling DSpark Training Using vLLM, Speculators and Mooncake | Red Hat | Ray Summit 2026
Building an AI Science Factory | Andrew Beam (Lila Sciences) | Ray Summit 2026
Power, Financing, and Multi-Silicon: How AI Clouds Bring Capacity Online Fast
vLLM in 2026: Challenges and Optimizations
Serving LLMs for Agents at Scale with Ray + vLLM | JPMorgan Chase | Ray Summit 2026
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Last Updated: October 3, 2026
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Summary
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 ... Training trillion-parameter MoE models on long-horizon agentic tasks demands that inference and optimization scale ... Only about 5% of scientific hypotheses can be verified cheaply. The rest require real experiments, which makes most of science ... How fast can you actually stand up GPU capacity, and what breaks along the way? At AI Infra As LLMs grow in size, context length, and architectural complexity, Serving LLMs for agentic workloads at Chase scale means balancing latency, cost, and resilience. The team cut latency from ...
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