OpenAI’s growing dissatisfaction with some Nvidia AI chips highlights a strategic shift toward faster inference hardware, testing Nvidia’s dominance in the AI boom.
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| OpenAI’s chip strategy reveals how speed, memory, and inference are reshaping competition in the AI hardware market. Image: CH |
Tech Desk — February 3, 2026:
OpenAI’s search for alternatives to some of Nvidia’s latest artificial intelligence chips marks a turning point in the relationship between two of the most influential players in the AI boom. While Nvidia remains central to OpenAI’s operations, the dissatisfaction described by sources reflects a deeper technological and strategic shift now reshaping the industry.
The tension is not about training large AI models, where Nvidia’s graphics processing units remain dominant, but about inference—the stage at which trained models generate responses for users in real time. As products like ChatGPT scale to millions of daily interactions, inference performance has become critical. Speed, memory access, and cost efficiency increasingly matter as much as raw computational power.
Sources say OpenAI has grown frustrated with how quickly Nvidia’s hardware can deliver responses for certain tasks, particularly software development and AI-to-software communication. These issues reportedly became most visible in Codex, OpenAI’s coding-focused product, where users place a premium on low latency and rapid feedback. Internally, some staff attributed performance bottlenecks to the limitations of GPU-based architectures optimized for training rather than inference.
This helps explain OpenAI’s interest in chips designed with large amounts of SRAM embedded directly into the silicon. Such designs reduce reliance on external memory, which can slow inference by forcing chips to spend more time fetching data rather than processing it. Traditional GPU architectures from Nvidia and AMD depend heavily on external memory, creating latency trade-offs that become more pronounced as inference workloads grow.
OpenAI’s evolving needs have also complicated high-stakes investment talks with Nvidia. A deal that could involve up to $100 billion was expected to close quickly but has instead dragged on for months, as OpenAI’s shifting product roadmap changed the type of computing resources it requires. Publicly, executives from both companies have sought to downplay any rift, emphasizing mutual reliance and ongoing collaboration.
At the same time, OpenAI has explored partnerships with specialized chipmakers such as Cerebras and Groq, which focus on inference-optimized designs. Those discussions underscore how inference has emerged as a new battleground in AI hardware. Nvidia’s response—licensing Groq’s technology and recruiting its chip designers—suggests the company recognizes the strategic importance of this shift and is moving aggressively to defend its position.
The broader competitive landscape adds pressure. Rivals like Google and Anthropic benefit from in-house hardware such as Google’s tensor processing units, which are designed specifically for inference-heavy workloads. OpenAI, lacking its own custom silicon, is more exposed to the architectural limits of external suppliers as its services diversify beyond general chat into coding, reasoning, and agent-based systems.
Despite the exploration of alternatives, OpenAI has been careful to stress that Nvidia still powers the vast majority of its inference fleet and delivers strong performance per dollar at scale. Sources suggest OpenAI is aiming to supplement Nvidia hardware, not replace it, with alternative chips eventually meeting around 10% of its inference needs.
Even so, the implications are significant. Inference is continuous, user-driven, and potentially larger in scale than training, making it a crucial source of long-term value. OpenAI’s dissatisfaction signals that the next phase of AI competition will not be decided solely by who can train the biggest models, but by who can run them fastest, cheapest, and most efficiently. For Nvidia, the challenge is clear: maintaining dominance in an AI world that is rapidly moving beyond training and into everyday, real-time use.