vLLM

vLLM on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on vllm in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around vllm, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Machine Learning

An open handbook on LLM inference at scale (GPU internals, KV cache, batching, vLLM/SGLang/TensorRT-LLM) [P]

Delve into the complexities of Large Language Model (LLM) inference at scale with a new, open handbook exploring GPU internals, KV cache optimization, batching strategies, and frameworks like vLLM, SGLang, and TensorRT-LLM. This evolving resource, currently detailing GPU execution and memory bottlenecks with clarifying diagrams, offers a practical, in-progress guide for those seeking to optimize performance. For deeper insights into attention mechanisms, see NonGameCatharsis's work on softmax-free attention models. Contributions and feedback are welcomed—explore the project and share your expertise at [github.com/harshuljain13/llm-inference-

Machine Learning

What is Speculative Decoding? (trending on paperswithco.de) [R]

Trending on Papers with Code: Speculative Decoding is an inference optimization technique gaining significant traction for large language models (LLMs). This method utilizes a smaller "draft" model to rapidly propose potential tokens, which are then verified by a larger, more precise model. This parallel processing dramatically accelerates token generation without compromising output quality. SGLang recently demonstrated state-of-the-art inference speeds using Modal and DFlash, highlighting its practical impact. Explore the technique and its related research at paperswithcode.co/methods/speculative-decoding, and consider "Next-Latent Prediction Transformers" for deeper insights.

Towards Data Science

The Infrastructure Behind Making Local LLM Agents Actually Useful

In "The Infrastructure Behind Making Local LLM Agents Actually Useful," we delve into the essential components that enhance the utility of local open-weight models, emphasizing the importance of fast and reliable scientific agents. By leveraging vLLM and long-context infrastructure, we uncover key lessons on optimizing performance and usability. This exploration is particularly relevant for those navigating the complexities of local LLMs. For further insights, check out "EmoNet: Speaker-Aware Transformers for Emotion Recognition," which reflects on the evolution of LLMs and their impact on the field.