Part 2: System-Level and Deployment-Centric Optimization for LLMs In Part 1, we looked at the internal mechanics of big language models, concentrating on architectural strategies such as sparse attention, cache compression, and speculative decoding to reduce compute and memory costs during inference. However, even the most intelligent model optimizations can fail if the underlying system […]

Part 1: Architectural and Algorithmic Optimisations in LLM Inference What 20+ Papers and Open-Source Projects Taught Me About Cracking LLM Inference If you’re developing AI solutions and hosting foundation models built around large language models (LLMs), you should think about the cost of serving them. However, money is not the only consideration; believe me, if […]