What Is OpenAI's Jalapeño AI Chip? A Simple Guide for Beginners
AI is not only about powerful models like ChatGPT. The hardware running those models also plays an important role.
In June 2026, OpenAI introduced Jalapeño, its first custom AI inference chip, developed with Broadcom. On August 25, 2026, OpenAI shared its first measured performance results, showing that Jalapeño can deliver high AI performance while using power efficiently.
So, what exactly is Jalapeño, why did OpenAI build its own chip, and does it mean OpenAI is trying to replace NVIDIA?
Let's take a simple look.
What Is OpenAI's Jalapeño AI Chip?
Jalapeño is OpenAI's first custom AI chip designed specifically for AI inference.
AI inference is the process of using a trained AI model to produce an answer or prediction from new input.
For example, when you ask ChatGPT a question, the model processes your request and generates a response. The computing work involved in producing that response is called inference.
A simple way to think about it is:
Training = studying for a test
Inference = taking the test
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| Figure 1. How AI Processes a User Request |
Training teaches an AI model by processing large amounts of data. Inference happens later, when the trained model is used to answer questions, generate text, or perform other tasks.
Jalapeño is designed to make this inference process faster and more power-efficient, especially for large language models (LLMs).
→ Source: OpenAI — Jalapeño chip overview and development
Why Is OpenAI Making Its Own AI Chip?
Running AI services at a large scale requires a huge amount of computing power.
Every time millions of people use an AI service, servers need to process their requests. As AI models become larger and AI usage increases, the amount of computing capacity needed also grows.
This makes the cost and efficiency of AI hardware increasingly important.
One reason OpenAI is developing its own chip is to optimize hardware specifically for the workloads its AI models need.
Instead of using only general-purpose AI accelerators, OpenAI can design parts of the hardware and software together around the way its models are actually used.
This could help OpenAI improve:
- Performance
- Power efficiency
- Response times
- Computing costs
- The amount of AI work that can be done with available power
However, this does not mean OpenAI can simply stop using NVIDIA GPUs. Jalapeño is designed mainly for inference, while AI training requires different types of computing resources.
→ Source: OpenAI — Jalapeño chip overview and development
How Does Jalapeño Relate to Broadcom?
OpenAI did not build Jalapeño completely on its own.
OpenAI and Broadcom worked together to develop the chip.
OpenAI designed Jalapeño around the needs of its AI models and inference workloads. Broadcom and Celestica helped with chip implementation, networking, boards, rack systems, and scalable production.
This partnership is important because designing an advanced AI chip involves much more than creating the chip architecture. The chip also needs to work with memory, networking, software, servers, and data-center systems.
In simple terms, OpenAI focused on what the chip needed to do, while its hardware partners helped turn that design into a production-ready system.
→ Source: OpenAI — OpenAI and Broadcom's custom AI chip partnership
How Fast and Efficient Is Jalapeño?
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| Figure 2. AI Hardware Performance and Efficiency |
OpenAI published its first measured performance results on August 25, 2026.
The company tested Jalapeño using the public InferenceX benchmark and compared it with commercially available AI systems under the tested conditions. The tests included GPT-OSS 120B, DeepSeek R1, and Kimi K2.5 1T.
Across these tests, OpenAI reported that Jalapeño delivered:
1.5 to 1.9 times more AI work per watt
and
1.7 to 3.6 times lower end-to-end latency
than the comparison systems. OpenAI also reported higher performance for highly interactive workloads.
→ Source: OpenAI — Jalapeño first performance results
What do these numbers mean?
Performance per watt measures how much useful AI work a system can perform for the amount of power it uses.
Higher performance per watt can be important for large data centers because running thousands of AI chips consumes a huge amount of electricity.
Latency is the time it takes for a system to respond.
Lower latency generally means a faster and more responsive AI experience.
For AI agents and interactive applications, this can be especially important because users may need to wait for many individual steps to finish.
Is Jalapeño Better Than NVIDIA?
This is where some caution is needed.
OpenAI's results show that Jalapeño performed very well against the comparison systems in the published tests. However, this does not mean Jalapeño is simply a better chip than every NVIDIA product.
The results were based on specific models, workloads, benchmark conditions, and comparison systems.
So it would be misleading to say:
“Jalapeño is better than NVIDIA.”
A more accurate statement is:
OpenAI's published tests showed that Jalapeño achieved higher performance per watt and lower latency than the NVIDIA-based comparison systems used in those tests.
This distinction matters because AI chips are designed for different purposes, and performance can change depending on the model, workload, software, and system configuration.
→ Source: OpenAI — Jalapeño benchmark results and comparison
Is OpenAI Competing With NVIDIA?
In some ways, yes. But the relationship is more complicated than a simple competition.
OpenAI is developing its own AI chips, but it is not replacing NVIDIA. The company says it will continue to use NVIDIA and other accelerators as part of its broader computing strategy.
At the same time, OpenAI is developing its own hardware so it can have more control over the infrastructure used to run its AI models.
Jalapeño is therefore better understood as an additional part of OpenAI's computing strategy, rather than a complete replacement for NVIDIA hardware.
OpenAI's first Jalapeño chip is also focused on inference. It is not designed to replace all of the hardware needed for AI development and training.
→ Source: OpenAI — OpenAI's broader AI infrastructure strategy
Why Does AI Inference Matter So Much?
AI inference happens every time an AI service responds to a user.
As more people use AI services, the number of inference requests can grow rapidly.
That means even a small improvement in efficiency can become important when it is multiplied across a huge data center.
For example, if a chip can process more AI work while using less power, an AI company may be able to serve more users with the same amount of electricity.
This is one reason companies such as OpenAI are increasingly interested in custom AI chips.
What Makes Jalapeño Different?
Jalapeño was designed specifically around the needs of modern large language models.
OpenAI says the chip was designed together with its memory, networking, software, and larger computing systems instead of treating the chip as an isolated piece of hardware.
The goal is to reduce unnecessary data movement and make better use of computing, memory, and networking resources during inference.
This is important because generating an AI response is not just about raw computing power. Data also needs to move between different parts of the system.
By designing more of these parts together, OpenAI hopes to make AI systems faster, more efficient, and less expensive to operate.
When Will Jalapeño Be Used?
OpenAI said Jalapeño is part of a multi-generation computing platform.
The company plans to begin deploying the platform at scale, with initial deployment targeted for the end of 2026 and expansion in the years that follow.
This means Jalapeño is not a one-time experiment. OpenAI describes it as the beginning of a longer hardware roadmap.
What Does Jalapeño Mean for the Future of AI?
Jalapeño shows that the AI industry is moving beyond the idea that AI progress depends only on better models.
Better AI also requires better infrastructure.
As AI models become larger and more people use AI services, companies need hardware that can deliver responses quickly without using excessive amounts of energy.
OpenAI's custom chip strategy could give the company more control over that infrastructure.
It could also help reduce costs and improve the speed and reliability of future AI services.
However, Jalapeño is still only one part of OpenAI's overall computing infrastructure. NVIDIA and other hardware providers will continue to play an important role in the AI ecosystem.
→ Source: OpenAI — Jalapeño development and deployment plans
Simple Summary
Jalapeño is OpenAI's first custom AI inference chip.
It was developed with Broadcom and designed specifically around the needs of large language models.
AI inference is the process of using a trained AI model to produce an answer or prediction.
OpenAI's first published tests showed strong performance per watt and low latency compared with the systems used in its benchmark comparisons.
However, Jalapeño should not be described as a complete replacement for NVIDIA. It is better understood as one part of OpenAI's long-term strategy to build more of its own AI infrastructure.
FAQ
Q. What is OpenAI's Jalapeño chip?
A. Jalapeño is OpenAI's first custom AI inference chip. It was designed to run large language models efficiently.
Q. What is AI inference?
A. AI inference is the process of using a trained AI model to generate an answer, prediction, or other output from new input.
Q. Why is OpenAI making its own chip?
A. OpenAI wants more control over its AI infrastructure and aims to improve performance, power efficiency, response times, and long-term computing costs.
Q. Did OpenAI build Jalapeño with Broadcom?
A. Yes. OpenAI and Broadcom worked together on the chip, with OpenAI designing the accelerator around its AI workloads and Broadcom providing semiconductor and system engineering expertise.
Q. Is Jalapeño replacing NVIDIA?
A. No. Jalapeño is not a complete replacement for NVIDIA hardware. It is a custom inference chip that OpenAI plans to use as part of its broader computing infrastructure.
Q. Is Jalapeño faster than NVIDIA?
A. OpenAI's published benchmark results showed higher performance per watt and lower latency than the NVIDIA-based comparison systems used in its tests. These results should not be interpreted as Jalapeño being universally faster or better than every NVIDIA chip.
Q. Why are custom AI chips becoming important?
A. As AI services grow, companies need more computing power while also trying to control electricity, hardware, and operating costs. Custom chips can be designed specifically for the workloads a company needs to run.
Final Takeaway
OpenAI's Jalapeño is not simply about creating another AI chip. It represents OpenAI's attempt to control more of the technology underneath its AI services.
The most important point for beginners is simple: AI models need powerful infrastructure to run, and OpenAI is now designing more of that infrastructure itself.
Jalapeño is an early step in that strategy, focused on making AI inference faster and more efficient.
Sources