How Inference Keeps Up with Our Insatiable Need for Tokens — Cerebras & Gimlet Labs
Daniel Kim of Cerebras and Michelle, co-founder of Gimlet Labs, explain how inference keeps getting faster and cheaper.
Proof1 independent outlet
Key points
- Google went from 480 trillion to 3.2 quadrillion tokens a month.
- NEXT UP: AI ENGINEER CODE SUMMIT
- Daniel covers the runtime: why decoding is memory-bound, splitting models across GPUs, continuous batching, KV cache, paged attention, prompt caching and chunked prefill.
- • Why decode is memory-bound and how big the KV cache gets
Sources (1)
- [1]How Inference Keeps Up with Our Insatiable Need for Tokens — Cerebras & Gimlet LabsAI Engineer (YouTube) · Oct 12, 05:36 AM
Daniel Kim of Cerebras and Michelle, co-founder of Gimlet Labs, explain how inference keeps getting faster and cheaper.
Google went from 480 trillion to 3.2 quadrillion tokens a month.
Extractive summary: sentences quoted from the sources.
Before this
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