AI Infrastructure Private

Groq

Fastest LLM inference chips in the world

Founded 2016 Mountain View, CA 300+ employees Series D ($640M raised) Cloud inference + Hardware
Funding Status
Series D ($640M raised)
Private Company

About Groq

Groq designs custom chips called Language Processing Units (LPUs) built specifically for AI inference — running trained models, not training them. Founded by Jonathan Ross, who previously designed Google's first TPU, Groq's chips deliver the fastest large language model inference speeds available, generating tokens 10-18x faster than GPU-based alternatives.

Groq's approach is fundamentally different from GPUs. While NVIDIA GPUs are general-purpose parallel processors adapted for AI, Groq's LPU is a deterministic, low-latency architecture designed from scratch for the sequential token generation that LLMs require. This specialization eliminates the memory bandwidth bottleneck that limits GPU inference speed.

The company offers its inference through GroqCloud, where developers can run Llama, Mixtral, and other open models at unprecedented speeds. Groq's free API tier has attracted hundreds of thousands of developers, and the company is building out data center capacity to serve enterprise inference workloads at scale.

Products & Services

LPU (Language Processing Unit)

Custom chip designed specifically for LLM inference. Deterministic architecture eliminates memory bottlenecks.

Hardware

GroqCloud

Cloud inference API running Llama, Mixtral, and other models at industry-leading speeds. Free tier available.

Cloud Service

GroqRack

Enterprise inference server with LPU chips for on-premise deployment of high-speed AI inference.

Hardware

Leadership

J
Jonathan Ross
Founder & CEO
Designed Google's first TPU. Founded Groq to build inference-specific chips.

Notable Achievements

  • Fastest LLM inference commercially available — 10-18x faster than GPUs
  • Founded by the designer of Google's first TPU
  • GroqCloud free tier attracted hundreds of thousands of developers
  • LPU architecture eliminates GPU memory bandwidth bottleneck
  • Raised $640M to build inference-specific data centers

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