[Podcast] Unboxing LLM Workloads: The three types of LLM workloads and how to serve them скачать в хорошем качестве

[Podcast] Unboxing LLM Workloads: The three types of LLM workloads and how to serve them

Unboxing LLM Workloads: The three types of LLM workloads and how to serve them https://modal.com/llm-almanac/workloads The provided text outlines the evolving landscape of LLM engineering, arguing that the dominance of proprietary APIs is fading in favor of customized, open-source inference. It categorizes AI workloads into three distinct types: offline, which prioritizes high throughput for batch processing; online, which demands ultra-low latency for human interaction; and semi-online, which requires flexible scaling for bursty traffic. To optimize these systems, the author recommends specific tools like vLLM for efficiency and SGLang for speed, while highlighting hardware strategies such as tensor parallelism and speculative decoding. Ultimately, the source serves as a technical guide for developers to architect their own infrastructure to achieve better cost-performance ratios. Through techniques like GPU snapshotting and multi-tenancy, the text demonstrates how organizations can move beyond flat-rate APIs to gain deeper control over their machine learning operations. #llm #inference #engineering Disclaimer: This video is generated with Google's NotebookLM.