The proliferation of large language models (LLMs) demands inference systems with both low latency and high efficiency at scale. GPU-based serving relies on HBM for model weights and KV caches, creating a memory bandwidth bottleneck during decode. To break through this bottleneck, we present the first large-scale, SRAM-based LLM inference deployment—Groq’s public cloud—serving hundreds of billions of tokens daily. This paper reviews Groq’s first-generation SRAM-based Huge Inference Pipelines (SHIP), highlighting: (1) a synchronous, low-diameter interconnect enabling low-latency scaling across thousands of chips; (2) optimizations for LLM serving under limited memory capacity; and (3) a large pipeline design that sustains efficiency and latency under varying prefill-to-decode ratios and context lengths. Together, these yield state-of-the-art latency while maintaining efficiency across diverse traffic scenarios—key to real-world LLM serving.