OpenAI has announced a major breakthrough with its first self-developed inference chip, codenamed Jalapeño, which outperforms leading NVIDIA systems across multiple large language models, including Moonshot AI's Kimi K2.5. The chip was designed specifically to handle the computational demands of running AI models after they've been trained, solving a persistent engineering challenge: how to serve more user requests faster without sacrificing energy efficiency.

What Makes Jalapeño Different From Other AI Chips?

Most AI hardware systems face a difficult trade-off. They can either process many requests quickly in batches, which improves overall efficiency but makes individual users wait longer, or they can prioritize speed for single requests, which wastes computing resources. Jalapeño attempts to break this trade-off by optimizing both metrics simultaneously through a co-designed approach that integrates the chip, memory, network, and software into a unified system.

The chip was developed in partnership with Cerebras, a hardware company specializing in AI accelerators. OpenAI emphasized that this collaboration enabled a unique architecture that adapts to different stages of how large language models process information. During the initial processing phase, models are compute-intensive; during the response generation phase, they become memory-bandwidth limited. Jalapeño's design accounts for both scenarios, keeping model data locally stored to minimize delays.

How Does Jalapeño Perform Against NVIDIA's Best Systems?

OpenAI tested Jalapeño against NVIDIA's GB200 and GB300 systems on three public models to demonstrate its versatility. The tested models included OpenAI's GPT-OSS 120B, DeepSeek's R1 670B, and Moonshot AI's Kimi K2.5 1T. The results showed consistent advantages across different workload types.

• Peak Throughput Efficiency: Jalapeño achieved 1.5 to 1.9 times more AI work per watt compared to current best-in-class systems, with the Kimi K2.5 showing a 1.5x improvement.
• Response Speed: End-to-end latency was reduced to between 1/1.7 and 1/3.6 of comparison systems, meaning users receive responses significantly faster; on Kimi K2.5, latency dropped by approximately 3.4 times.
• Interactive Workloads: For highly interactive applications where speed matters most, Jalapeño demonstrated performance advantages ranging from 2.1 to 4.1 times better than competing systems.
• Power Consumption: The chip has a rated power consumption of 700 watts but typically operates at 550 watts or below during actual workloads, demonstrating efficient real-world performance.

The inclusion of Moonshot AI's Kimi K2.5 in OpenAI's testing is particularly significant. Kimi K2.5 is a 1 trillion parameter model, making it one of the largest language models in existence. The fact that Jalapeño was tested and optimized for external models, not just OpenAI's own systems, suggests the chip is designed as a general-purpose inference solution rather than proprietary hardware locked to a single company's software.

One striking benchmark emerged when testing DeepSeek R1 670B at low latency settings. At the same decoding speed where NVIDIA's GB300 achieved its previous best performance, Jalapeño reached a throughput of 12,258 mixed tokens per second per kilowatt, compared to GB300's 118, representing a 104.3x advantage in that specific scenario.

Why Should Companies Care About This Development?

For organizations running massive AI services, the economics of inference chips matter enormously. Every millisecond of latency reduction and every percentage point of energy efficiency improvement translates directly to cost savings and better user experience. OpenAI's move into chip design reflects a broader industry trend: companies like Google, Amazon, Microsoft, and Meta have all developed custom AI chips because general-purpose graphics processing units (GPUs), while flexible, are not optimized for specific AI workloads.

The timing is also relevant to the emerging era of AI agents, which are systems that complete multi-step tasks autonomously. When an agent must perform 10 sequential steps, even small latency improvements in each step compound significantly. A system that reduces latency by 3.4 times on a single model could cut total task completion time by hours for complex workflows.

How to Evaluate AI Inference Hardware for Your Organization

• Measure Both Throughput and Latency: Don't focus on peak performance alone; evaluate how systems perform under realistic mixed workloads where some requests need fast responses and others can tolerate batching.
• Calculate Cost Per Task: Compare total cost of ownership by multiplying power consumption by electricity rates and dividing by useful work completed, rather than comparing raw specifications.
• Test With Your Actual Models: Performance varies significantly across different model architectures and sizes; request benchmarks using models similar to what your organization plans to deploy.
• Consider Latency Requirements: Determine whether your use case prioritizes interactive speed or batch efficiency, as this dramatically affects which hardware architecture makes sense for your needs.

OpenAI's announcement also signals a strategic shift in how the company views its competitive position. By developing custom silicon, OpenAI is attempting to control more of the AI stack, from model development through training to inference and deployment. This vertical integration could provide cost advantages and performance benefits that are difficult for competitors to match.

The fact that Jalapeño was tested successfully on Moonshot AI's Kimi K2.5 demonstrates that OpenAI is positioning the chip as a platform play rather than a proprietary advantage. By showing strong performance on external models, OpenAI may be signaling its willingness to license or commercialize the technology, potentially creating a new revenue stream while establishing industry standards around inference efficiency.