OpenAI's «Jalapeño» Chip: Ultra-Fast Inference Performance at Scale

In a move that reflects AI companies' ambition to design their own hardware, OpenAI has revealed new details about its chip named «Jalapeño», during the Hot Chips conference specialized in processor technologies. For the first time, the company presented preliminary benchmark results that offer a glimpse into the capabilities of this system, purpose-built for inference operations in large-scale AI models.

Performance That Surpasses Leading Benchmarks

The «Jalapeño» chip was tested using SemiAnalysis's InferenceX benchmark, and the results showed notable superiority across two key metrics compared to the latest inference processors currently available on the market:

• A greater number of tokens per user, meaning faster responses.
• Higher throughput per kilowatt, i.e. greater energy efficiency.

Richard Ho, head of hardware at OpenAI, explained during a press call that the core takeaway is «a very significant leap in performance compared to the latest available offerings». He added that the chip is capable of accomplishing more AI tasks per unit of energy, while delivering faster responses at the same time, making it effective at serving large numbers of customers with very low latency.

A Comparison That Requires Careful Reading

It is worth noting that this comparison was conducted against Nvidia's Blackwell system. However, this superiority should be placed in its proper temporal context; the chip will not reach the full deployment stage before the end of 2026 «and in very limited quantities» according to Ho's estimate, with broader deployment beginning during 2027. This means the competition may have made significant strides by then, making the current comparison more of an early indicator than a final verdict.

A Strategic Partnership with Broadcom

The «Jalapeño» project was first announced last October, and was developed through close collaboration between OpenAI and Broadcom. Notably, OpenAI's own models contributed to the development process, serving as a practical example of using AI to design the hardware that will later run it.

The company plans to make «Jalapeño» a multi-generational platform, where AI products, models, chips, and memory units are all developed in an integrated and coordinated manner—an approach known as «full-stack».

Addressing Inference Bottlenecks

This integrated approach allowed OpenAI to address specific stages of the inference process that often cause slowdowns or friction in processing. The company focused specifically on reducing latency in two critical stages:

• The prefill stage.
• The communication and data transfer stage between system components.

The company noted in an official post that the chip was designed to minimize data movement and communication latency as much as possible. This means that the model's state, including the cache known as the KV cache used during response generation, can be placed and retained locally, while the system activates the appropriate mix of compute, memory, and networking capabilities for each stage of inference separately.

What Does This Mean for the Future of AI?

The development of «Jalapeño» reflects an accelerating trend among major AI companies toward owning their own hardware, with the goal of reducing reliance on external suppliers and lowering the enormous operational costs associated with running large models. While the announced figures are still early results that need to be verified in real-world operating environments, they paint a clear picture of the industry's direction: combining software and hardware into a unified design that serves a single purpose with maximum efficiency. The open question remains how well this superiority will hold up against competitors' advances by the time of full deployment in 2027.

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