LLM Inference Internals: KV Cache, PagedAttention, and Continuous Batching
“Deep dive into making LLMs fast. Understand the KV cache, memory management, and scaling inference. This comprehensive curriculum is designed to give you hands-on experience with LLM Inference Internals: KV Cache, PagedAttention, and Continuous Batching.”
About this Course
What you'll learn
Transformer Inference Fundamentals
Understand the bottleneck in autoregressive decoding and the role of the KV cache.
Memory Management & PagedAttention
Learn how vLLM optimizes memory using operating system paging concepts.
Batching Strategies
Explore methods to maximize GPU utilization across multiple requests.
Quantization and Low Precision Inference
Learn techniques to run models in lower precision without significantly losing accuracy.
Serving at Scale
Deploy optimized models and benchmark their performance.
PagedAttention & Memory Management
Implement vLLM-style PagedAttention to eliminate memory fragmentation in the KV cache.
Continuous Batching & Serving
Build a continuous batching scheduler to maximize GPU utilization across concurrent requests.
Learn
Watch curated videos and read study resources
Practice
Practice what you learned
Build Projects
Build projects using your new gained knowledge
Submit & Verify
Submit your project and get verified by our system
References
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