Model Inference
Model Inference on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on model inference 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 model inference, 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.
KV cache as an agent runtime [R]
Our research explores a compelling alternative for enhancing LLM interactivity: leveraging the KV cache as an agent runtime. Recent work, detailed in a new post on our research blog, investigates modifying model inference states to achieve greater responsiveness—building on previous innovations like Hogwild! Inference and AsyncReasoning. We're previewing future work where a Qwen3.8-27B agent actively engages with a DOOM environment using these techniques. This suggests that optimizing model runtime design could unlock significant agent capabilities, bridging the gap between model architecture and harness limitations.
Presentation: From Fab To Token - The State Of The Market
Jordan Nanos’s presentation, “From Fab to Token – The State of the Market,” delivers a critical analysis of how current semiconductor limitations, burgeoning data center demands, and networking bottlenecks are reshaping AI software architecture. Drawing on insights from SemiAnalysis research, Nanos explores benchmark performance, GPU scaling, and the complex interplay of tokenomics across the entire AI pipeline—from chip fabrication to model inference. Understand the tangible impacts on AI development, as highlighted by considerations like those explored in our recent piece, "Three Generations of Autoscaling."