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Scaling LLM Workloads on TPUs with Ray: From Pre-Training to Inference | Google | Ray Summit 2026
Embedding models with vLLM TPU
🎙️Simon Mo CEO of @inferact: 5 new VLLM features in 2026!
vLLM and the State of AI Inference | Simon Mo (Inferact) | Ray Summit 2026
Scaling DSpark Training Using vLLM, Speculators and Mooncake | Red Hat | Ray Summit 2026
Serving LLMs for Agents at Scale with Ray + vLLM | JPMorgan Chase | Ray Summit 2026
AMD and vLLM: What's New | AMD | Ray Summit 2026
TensorRT-LLM vs vLLM: Throughput Benchmarks 2026
PyTorch loves vLLM | Meta | Ray Summit 2026
vLLM Explained in 2 Min [2026] | 2 Min Series of Tech |
Serving vLLM on Intel GPUs, CPUs, and Gaudi | Intel | Ray Summit 2026
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Last Updated: October 3, 2026
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Agentic production traffic brings long multi-turn sessions, heavy prefix reuse, and bursty, heterogeneous requests that challenge ... Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam ... Learn more: goo.gle/4ijMNkj Learn how to serve text embedding State-of-the-art speculative decoding lives or dies on the drafter, and training drafters well at scale is a production issue. At Serving LLMs for agentic workloads at Chase scale means balancing latency, cost, and resilience. The team cut latency from ... Head-to-head benchmark comparison of TensorRT-LLM (NVIDIA) vs The High-Throughput and Memory-Efficient inference and serving engine for LLMs Easy, fast, and cost-efficient LLM serving for ...
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