AI Engineer Video: Experts from Cerebras and Gimlet Explain Inference Optimization
AI Engineer channel released a video featuring Daniel Kim, Head of Growth at Cerebras, and Michelle, Co-founder of Gimlet Labs, discussing techniques to accelerate and reduce the cost of inference amid surging token demand. Suitable for engineering managers and technical decision-makers focused on AI infrastructure efficiency.
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The AI Engineer channel published a video titled "How Inference Keeps Up with Our Insatiable Need for Tokens — Cerebras & Gimlet Labs" on October 12, 2026, recording a talk from the AI Engineer World's Fair 2026 in San Francisco. The speakers are Daniel Kim, Head of Growth at Cerebras, and Michelle, Co-founder of Gimlet Labs. According to the publisher's description, the video explains how the industry uses technical methods to cope with the surge in monthly token processing volume, such as Google's increase from 480 trillion to 3.2 quadrillion tokens.
The chapter list provided by the publisher indicates that Daniel Kim focuses on runtime-level optimizations, including why decoding is memory-bound, splitting models across GPUs, continuous batching, KV cache management, paged attention, prompt caching, and chunked prefill. Michelle covers system-level aspects, including prefill/decode disaggregation, speculative decoding, attention/FFN disaggregation, and introduces Gimlet's multi-silicon inference cloud along with agents that write optimized kernels. For engineering team leads or architects needing to understand the current evolution path of AI inference infrastructure technologies, this structured topic list offers clear viewing guidance.