Wall Street Journal covered Groq's 3 LPX chip, designed to reduce latency accumulation in multi-step agent workflows via deterministic scheduling and 128GB SRAM
Following·since 25 Aug 2026·Day 2·2 sources
CoreWeave and Groq Address Agentic AI's Distinct Infrastructure Demands
The gist
Sequential agent steps cause decoding delays to accumulate across a workflow, and the unpredictable traffic from thousands of concurrent agent chains stresses network fabric shared with training and inference. Both network and chip designs are being restructured to address these requirements.
The full picture
Agentic AI's sequential, multi-step inference patterns impose infrastructure requirements different from traditional AI workloads. CoreWeave published five lessons from building a non-blocking, multi-plane network fabric designed for thousands of concurrent agent chains running alongside training and inference workloads. Groq's 3 LPX processor targets the latency accumulation problem in multi-step agent tasks, using deterministic compiler scheduling and 128GB of SRAM across the rack.
How it developed
CoreWeave published five lessons from building its non-blocking, multi-plane network fabric for agentic AI workloads
Sources
CoreWeave Twitter
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