Nvidia unveils AI chip with Groq technology at GTC 2026 as OpenAI commits major capacity

Nvidia Prepares Dedicated AI Inference Chip for GTC 2026 Launch

Nvidia is developing a specialized AI inference chip that will debut at its annual GTC developer conference in San Jose next month, according to reports from the Wall Street Journal 1

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. The new platform represents a strategic shift for the chipmaker as it addresses mounting pressure in inference computing, where rivals like Google and Amazon Web Services have already deployed specialized chips that compete with Nvidia's traditional GPUs 1

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. The processor is designed to help OpenAI and other customers accelerate AI processing and run pre-trained AI models more efficiently in production environments.

$20 Billion Licensing Deal Brings Groq Technology to Nvidia

The upcoming NVIDIA-Groq AI chip integrates technology from startup Groq, which Nvidia acquired through a $20 billion licensing deal in December 1

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. As part of the arrangement, Nvidia hired Groq's founding CEO Jonathan Ross and President Sunny Madra in what was billed as one of Silicon Valley's largest-ever acqui-hires. Groq's chips, known as Language Processing Units or LPUs, are built on an entirely novel architecture that enables inference tasks with significantly lower energy consumption compared to traditional GPUs 1

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. This licensing deal effectively shut down OpenAI's talks with other inference chip providers including Cerebras and Groq itself 2

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OpenAI Commits 3GW of Dedicated Inference Capacity

OpenAI has secured early access to Nvidia's new AI inference chip and will become one of its largest customers, committing 3GW of dedicated inference capacity 3

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. This represents a major win for Nvidia, particularly as OpenAI had been actively shopping for more efficient alternatives to address latency-sensitive AI workloads. The ChatGPT maker has been dissatisfied with the speed at which Nvidia's current hardware can deliver answers for specific problems such as software development and AI-to-AI communication 2

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. OpenAI reportedly needs new hardware that would eventually provide about 10% of its inference computing needs in the future 2

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. The company plans to use the new chip to power its Codex programming tool, which competes with Anthropic's Claude Code in the lucrative AI coding market 1

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Addressing the Bottleneck in AI Decoding and Energy Consumption

The shift toward dedicated inference processors addresses a critical bottleneck in AI decoding that has plagued the industry 4

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. While Nvidia's GPUs remain dominant for training AI models, they are no longer considered the most efficient option for running AI applications in production. Many companies have found that Nvidia's chips consume excessive energy, making them costly for applications like AI agents that require immense computing power to carry out tasks autonomously 1

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. This efficiency gap has driven companies like OpenAI to sign multibillion-dollar contracts with competitors such as Cerebras and SambaNova, which offer specialized inference-focused chips 1

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. The new platform could strengthen Nvidia's position in AI infrastructure by expanding its custom silicon strategy beyond traditional GPUs 4

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, reinforcing its leadership as demand for efficient computing power continues to surge across the rapidly evolving AI hardware ecosystem.