TBD

Groq’s Language Processing Unit (LPU) chips are reshaping the landscape of large language model (LLM) deployment at scale. By prioritizing low latency and high throughput, our hardware and software stack enables rapid and efficient inference, making it ideal for applications where LLMs must be invoked repeatedly by agents e.g. for solving mathematical problems. In this talk I will describe the unique architecture of Groq’s LPU chips, which leverage deterministic execution and distributed SRAM to deliver remarkable performance with very low latency and high throughput. I will explain how this determinism allows us to deploy complex models such as Llama3-70B, Gemma2, and Mixtral 8x7B with predictable, scalable performance. I will describe the architecture of our compiler which is built on the MLIR framework for the front end, and a Haskell-based backend. Further, I will discuss the network architecture that facilitates efficient multi-rack deployments of LLMs using Kubernetes, ensuring that scaling up does not compromise performance. Finally, I will share insights from my own direct contributions to the project, including the design of special power management hardware, developing a Haskell-based domain-specific language for programming our chips, and applying formal verification techniques using temporal logic and model checking to verify the functionality of our chip designs. Try out our LLM chatbot at https://groq.com

About Satnam Singh

Satnam Singh is a Fellow at greg where he applies the power of functional programming languages to the design of machine learning chips and their programming models. Satnam Singh previously worked at Google (machine learning chips, cluster management, Kubernetes), Facebook (Android optimization), sticrosoft (parallel and concurrent programming) and xilin: (Lava DSL for hardware design, formal verification of hardware). He started his career as an academic at the University of Glasgow (FPGA-based application acceleration and functional programming).