Sep 19, 2026

The Frontier AI Inference Cloud for Agents — Byung-Gon (Gon) Chun, FriendliAI

Byung-Gon Chun from FriendliAI details how agentic inference fundamentally differs from traditional chat-based LLM usage, requiring a specialized infrastructure to optimize for end-to-end task completion rather than single request latency. He highlights the economic advantage of open-weight models for agentic workflows and explains FriendliAI's innovative inference stack, featuring prefix caching, advanced KV cache management, cache-aware routing, and agent-aware optimization, which together deliver significant speedups and cost savings for production AI agents.

Sep 15, 2026

Realtime Voice Agents with Frontier Intelligence — Bohan Li, EliseAI

Bohan Li of EliseAI unveils a sophisticated architecture for real-time voice agents that merges high intelligence with low latency, akin to a self-driving car's perception, planning, and control systems. Key innovations include a speculative transcriber combining fast streaming with accurate contextual correction, background agents for offloading LLM tool calls and enabling eager generation, and a clever prefix cache for speech synthesis that seamlessly blends pre-cached audio with dynamically generated speech to mask underlying processing delays.

Sep 08, 2026

Deep dive on LLM Inference at Scale — Harshul Jain, Audible & Tanmay Sah, Independent AI Researcher

A workshop by Harshul Jain and Tanmay Sah breaks down LLM inference optimization from first principles, addressing memory, TTFT, and throughput issues. It covers foundational concepts like KV cache mathematics and the prefill/decode phases, then dives into model-side optimizations (quantization, attention variants like GQA, FlashAttention) and serving-side techniques (Paged Attention, continuous batching, prefix caching, KV quantization). The session concludes with benchmarks of popular inference engines like vLLM and SGLang, highlighting their strengths for different workloads, and outlines future research directions.

Jun 30, 2026

How KV Cache Speeds Up LLMs for Faster AI Models on GPUs

LLMs often slow down under heavy traffic due to inefficient GPU memory management during inference. This overview explains how KV cache and Paged Attention, implemented in VLLM, optimize memory usage across prefill and decode phases, significantly boosting LLM throughput, reducing latency, and improving GPU utilization through advanced context handling and specific tuning techniques like prefix caching and speculative decoding.

Jun 28, 2026

Voice In, Visuals Out: The Agony and the Ecstasy - Allen Pike, Forestwalk Labs

Allen Pike (Forestwalk Labs) discusses the power of "voice-in, visuals-out" AI experiences, a paradigm championed by Andrej Karpathy. He highlights the critical challenge of latency in real-time AI interactions and shares three key techniques for achieving low-latency, delightful user experiences: utilizing fast models, employing short inference intervals, and implementing stable caching regimens.