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LLM inference optimization: Architecture, KV cache and Flash attention
LLM inference Optimization: From Token to Scale
How LLM Inference Actually Scales: KV Cache, Batching & vLLM
What is Prompt Caching Optimize LLM Latency with AI Transformers
How the VLLM inference engine works
AI Agent Inference Performance Optimizations + vLLM vs. SGLang vs. TensorRT w/ Charles Frye (Modal)
Understanding LLM Inference | NVIDIA Experts Deconstruct How AI Works
AI Optimization Lecture 01 - Prefill vs Decode - Mastering LLM Techniques from NVIDIA
CMU LLM Inference (11): Agents and Multi-Agent Communication
Understanding the LLM Inference Workload - Mark Moyou, NVIDIA
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Last Updated: October 4, 2026
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Summary
Deploying Large Language Models into production requires solving real-world latency, memory, and cost bottlenecks. Today we have Philip Kiely from Baseten on the show. Baseten is a Series B startup focused on providing infrastructure for Open-source LLMs are great for conversational applications, but they can be difficult to scale in production and deliver latency ... Ready to become a certified watsonx Generative In this video, we understand how VLLM works. We look at a prompt and understand what exactly happens to the prompt as it ... Zoom link: us02web.zoom.us/j/82308186562 Talk Introductions and Meetup Updates by Chris Fregly and Antje Barth ... In the last eighteen months, large language models (LLMs) have become commonplace. For many people, simply being able to ... This lecture (by Graham Neubig) for CMU CS 11-763, Advanced NLP (Fall 2025) covers: Basic agent concepts and definitions ...
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