"Groq's chips are purpose-built for inference - the stage at which AI models use what they've learned in the training process to produce real-world results. Inference is where AI companies take their models from the lab to the bank. And with the soaring costs to train AI, those models better get to the bank soon. Cheap and efficient inference is essential for AI use at scale."
"In the training phase, models ingest vast datasets of text, images and video and then use that data to build internal knowledge. In the inference phase, the model recognizes patterns in data it's never seen before and generates responses to prompts based on those patterns. Think of the phases like a student studying for a test and then taking the test."
Nvidia's chips power much of AI training, while inference remains a bottleneck that Nvidia does not fully control. Groq produces chips purpose-built for inference, enabling trained models to generate real-world outputs efficiently. Cheap and efficient inference is essential for deploying AI at scale and turning costly trained models into revenue. Investors are funding inference startups to bridge experimentation and production. Improved inference could accelerate enterprise AI adoption, driving more model training and increasing demand for training hardware. Groq's founders and employees will join Nvidia while Groq operates independently under a non-exclusive licensing agreement resembling an acquihire.
Read at Axios
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