Continuous batching from first principles

1. 1Understand attention mechanisms in LLMs
2. 2Learn about KV caching and its role in optimizing LLM performance
3. 3Derive continuous batching by optimizing for throughput
4. 4Apply continuous batching to improve the efficiency of LLM models

Machine learning engineers and researchers can benefit from understanding continuous batching to improve the efficiency of their LLM models, while software engineers can apply this knowledge to optimize the deployment of these models

💡 Continuous batching is derived from attention mechanisms and KV caching to optimize LLM throughput

🤖 Continuous batching optimizes LLM throughput!