Groq is an AI / GPU Cloud company founded in 2016 and based in Mountain View, United States.
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A fully managed cloud inference platform providing developers with direct access to Groq's Language Processing Unit (LPU) clusters through a simple API. GroqCloud enables instant deployment and dynamic scaling of large language models and other AI workloads, supporting popular open-source models like LLaMA, DeepSeek, and Mistral. Developers benefit from ultra-low latency (sub-millisecond inference) and high throughput without managing underlying hardware. The platform uses a pay-per-token pricing model and currently serves over 2.5 million developers, making production-ready AI inference accessible through a developer-friendly interface with SDKs and integrations.
An enterprise-grade, on-premises deployment solution delivering high-density AI inference clusters with up to 64 interconnected Groq LPU chips organized in GroqNode servers. GroqRack provides deterministic performance with end-to-end latency of only 1.6 microseconds per rack, enabling seamless scaling across data centers without external networking switches. Designed for regulated industries, air-gapped environments, and organizations with data sovereignty requirements, GroqRack combines Groq's proprietary LPU architecture with integrated management software and standard air cooling to deliver production-scale AI inference capabilities with minimal operational complexity.
Groq's proprietary custom-designed ASIC accelerator chip purpose-built for AI inference workloads, particularly large language model execution. The LPU features a deterministic Tensor Streaming Processor architecture with co-located compute and memory on a single chip, eliminating traditional bottlenecks caused by separate caches and memory hierarchies. Unlike GPUs designed for graphics, the LPU delivers superior token-per-second throughput, ultra-low latency consistency, and energy efficiency for real-time AI applications. The architecture supports inference for LLMs, image classification, anomaly detection, and other computational tasks, with hundreds of megabytes of on-chip SRAM enabling high-bandwidth, low-latency access to model parameters.
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