Nvidia's $20 Billion Groq Acquisition Just Paid Off. This New Chip Could Change the AI Inference Game in 2026.

When **Nvidia **(NVDA 0.40%) paid $20 billion in cash in late 2025 for the artificial intelligence (AI) inference unit of chip start-up Groq – which is unrelated to Elon Musk’s chatbot Grok – some analysts were surprised by the hefty price tag.

But Nvidia CEO Jensen Huang clearly knows what he’s doing. “We plan to integrate Groq’s low-latency processors into the NVIDIA AI factory architecture,” he wrote at the time. And now, less than three months later, that plan has become a reality as Huang unveiled the Groq 3 LPX inference accelerator.

Here’s why this new product could change the AI inference game in 2026.

Image source: Nvidia.

Why AI inference chips matter

AI inference is nothing more than a fancy term for a trained AI model making decisions based on new data or inputs.

When ChatGPT generates a unique response to user input it has never seen before, it’s using inference. When a self-driving car analyzes real-time data from its sensors to determine whether it’s safe to accelerate, that’s inference too. Pretty much all the “work” any trained AI model does relies on inference.

Inference usually consists of two steps: prefill and decode. The prefill step is when the AI model processes a query, like a chatbot parsing a user’s question. The decode step is when the model formulates a response by accessing its accumulated training data and converting its findings into a legible answer or instruction.

“Inference chips” are processors and memory chips specifically optimized for speeding up AI inference tasks in a cost-effective manner.

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Why it’s a game changer

Groq specializes in language processing unit (LPU) technology, which allows an AI inference model to parse and sequence natural language inputs and outputs with low latency. The Groq 3 LPU uses static random access memory (SRAM) to increase an AI model’s interactivity. Meanwhile, Nvidia’s top-of-the-line Rubin GPUs utilize high-bandwidth memory (HBM), which allows an AI model to process more data more quickly. That increases throughput and makes the model “more intelligent.”

But even though the Rubin GPU’s 288GB of memory crushes the Groq LPU’s 500 MB, it only offers a pokey 22 TB per second of memory bandwidth compared to the Groq 3 LPU’s 150 TB per second. With the release of its Nvidia Groq 3 LPX inference accelerator, the company is combining an LPU’s interactivity with the Rubin platform’s throughput and performance to provide a superior agentic AI system for language-based inference models.

Image source: Getty Images.

How superior? Nvidia claims the Groq 3 will provide 35x higher throughput per megawatt for trillion-parameter AI models than its Blackwell NVL72. That doesn’t just reduce energy consumption, but is critical for marketability. If a user regularly has to wait more than a few seconds for a chatbot to respond, they’ll find another chatbot.

Companies that want to stay on the cutting edge of agentic AI chatbot technology will likely spend big for this kind of performance advantage. That should boost Nvidia’s sales and share price.

Once again, Nvidia has shown why it’s the undisputed leader among AI chipmakers.