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Mastering LLM Inference Optimization From Theory to Cost Effective Deployment: Mark Moyou
LLM Inference Optimization: How Quantization, Batching & Distillation Make AI Faster
Paged Attention and Continuous Batching: How vLLM Actually Works
Optimize LLM inference with vLLM
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Last Updated: October 27, 2026
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Summary
Deploying Large Language Models into production requires solving real-world latency, memory, and cost bottlenecks. We are doing a book club series for Open-source LLMs are great for conversational applications, but they can be difficult to scale in production and deliver latency ... Why does a 70B language model crawl at 8 tokens per second on one setup, then feel instant on another? The difference is ... Ready to become a certified watsonx How can a single GPU serve 50 users at virtually the same speed as one? In this video, we break down why naive static We build up how vLLM serves thousands of users from two pieces of bookkeeping: the scheduler and the KV cache memory ... Ready to serve your large language models faster, more efficiently, and at a lower cost? Discover how vLLM, a high-throughput ...
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