In plain words
Groq is a hardware and cloud company, not a chatbot. It is easy to mix up with Grok, Elon Musk's chatbot, but the two are unrelated. Groq, spelled with a q, makes the machinery that runs other people's AI models.
Its big idea is a chip called the LPU, short for Language Processing Unit. Most AI runs on GPUs, which keep the model's numbers in separate memory chips and keep fetching them. The LPU keeps them on the chip itself, in a carefully planned flow, so words come out quickly and steadily.
Groq does not mainly sell chips to the public. It runs GroqCloud, a service where developers send a question to an open-weight model, such as Meta's Llama, and get the answer back fast. This step, running a trained model, is called inference.
Everyday example
A kitchen with everything at arm's reach
One cook keeps ingredients in a storeroom down the corridor and walks back for each item. Another has every masala tin and vegetable laid out on the counter, in the order the recipe needs them. Both cook the same dish, but the second never waits. A GPU is closer to the first cook; Groq's LPU is built like the second.
Where the example breaks: A counter only holds so much. Each LPU has only a little memory, so a big model is spread across many chips working in step.
How it actually works
Speed in AI answers mostly comes down to how fast the chip can reach the model's numbers.
Memory on the chip
A GPU reads model weights from stacked high-bandwidth memory beside it. The LPU uses SRAM built into the chip, which Groq says is roughly ten times faster to reach than HBM: about 80 terabytes a second against about 8.
Planned in advance
Groq's compiler, the software that prepares a model for the chip, schedules every data movement before the program runs. Nothing waits on a busy shared resource, so each step takes a predictable time.
An assembly line across chips
Groq describes data moving on conveyor belts between the chip's units, and on to neighbouring chips, all in sync. A big model is split across many LPUs that pass work along like stations on a line.
What it is good for
The payoff is high tokens per second for each user and low latency, which matters for voice assistants, coding tools and agents that call a model many times in a row.
- Founded
- 2016, by Jonathan Ross and other engineers, several from Google's TPU chip team
- Known for
- Very fast answers from open-weight models, served through GroqCloud
- The chip
- First called the Tensor Streaming Processor, later renamed the LPU (Language Processing Unit)
- GroqCloud
- Opened to developers in February 2024; Groq reported one million developers on it in March 2025
Other companies chasing fast inference with special chips include Nvidia and Cerebras. See AI accelerators for the wider picture.
Questions people ask
Is Groq the same as Grok?
Did Nvidia buy Groq?
Not exactly. In December 2025 Nvidia agreed a non-exclusive licence to Groq's inference technology, reportedly worth about 20 billion dollars, and hired founder Jonathan Ross, president Sunny Madra and others. Groq said it would carry on as an independent company running GroqCloud.
What is an LPU?
A Language Processing Unit: Groq's name for its AI chip. It is designed for running trained models quickly, not for training them. Its main difference from a GPU is that the model's numbers sit in fast memory on the chip, in a fixed, pre-planned flow.
Why is Groq so fast?
Mostly because of memory. Writing each word means reading huge numbers of model weights, and the LPU keeps them on the chip instead of fetching them from separate memory. The work is also planned in advance, so the chip rarely sits idle.
Who uses Groq?
Mainly developers and companies building AI features, who reach GroqCloud through an API rather than a chat website. Groq has announced partners such as Paytm in India and Bell Canada in 2025. Ordinary users meet it inside other apps without seeing the name.
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