OpenAI Jalapeno Inference Chip vs NVIDIA GB300: Architecture, Efficiency, and Production Challenges
OpenAI's first custom inference chip Jalapeno reveals benchmarks vs NVIDIA GB300, covering HBM4 architecture, data movement optimization, and cost impact.
OpenAI recently announced performance benchmarks for its first custom inference chip, Jalapeno. In tests on the open-source inference benchmark InferenceX, the chip reportedly outperforms NVIDIA's currently publicly available representative system, GB300, in both speed and efficiency. This article is based on public interview information and outlines its key technical points, design trade-offs, and industry impact.
#1. Test Background and Core Conclusions
OpenAI's VP of Hardware, Richard Ho, said the team chose InferenceX as the benchmark because it is a neutral and open-source methodology. Unlike common marketing narratives, the comparison target this time is not NVIDIA's latest production system, Vera Rubin, but the already released and fully optimized GB300 data. OpenAI explained that this mainly considers data availability and comparability: GB300 already has mature public results, while Vera Rubin was just released and has not yet formed comparable optimized data.
From the results, Jalapeno's key feature is achieving both high throughput and low latency on the same device. OpenAI claims this is an industry first. High throughput means serving more users and tokens at a lower unit cost; low latency directly improves response speed, enabling agentic tasks to return faster and is suitable for scenarios such as high-frequency coding.
#2. Architecture Design: HBM4 and Data Movement Optimization
Jalapeno adopts HBM4 high-bandwidth memory and is among the first HBM4 products to enter mass production, closely following NVIDIA's related products in timing. Its design approach is described as a "blank-sheet" approach: OpenAI's hardware team collaborated with internal research teams to identify the main bottlenecks in large language model inference, with particular attention to data movement. Unlike general-purpose GPU or TPU architectures, Jalapeno builds a tighter integration between memory and compute cores, achieving lower latency through reduced data shuffling and algorithm optimization. OpenAI also calls this design a "novel architecture" and emphasizes that no other vendor currently uses the same approach.
#3. Why Focus on Inference Rather than Training
Richard Ho explained that OpenAI has huge demand for compute resources, but the growth is mainly concentrated on the inference side. For training, there are still strong partners like NVIDIA providing chips, so the in-house silicon project prioritizes inference chips to expand OpenAI's available inference compute capacity. As the user base grows rapidly, the inference side needs a more diversified product portfolio. After Jalapeno joins, it will form OpenAI's inference compute pool together with other chips provided by partners.
#4. Full-Stack Control and Programmability
The key advantage of custom silicon lies in full-stack control: joint optimization can be carried out from the model, software, to the silicon level. OpenAI believes that as a frontier AI lab, this end-to-end control capability is a unique advantage, allowing developers to make trade-offs at different stages and achieve efficiency that is difficult to obtain by merely purchasing external chips.
New architectures often come with concerns about software ecosystem adaptation. OpenAI said that just a few months after receiving the chip, its team had successfully run three quite different models on the new hardware, which reflects that the programming model is relatively developer-friendly and also shows that its AI models already have some automation capability in optimizing firmware and kernels.
#5. Cost and Production: Performance per Watt and Supply Chain Bottlenecks
In terms of cost per token, OpenAI did not provide an absolute figure but said that performance per watt is a good proxy indicator for token cost. Jalapeno's performance per watt is currently about 1.8 to 4 times that of other existing chips. OpenAI expects that as the chip enters production deployment, customer token prices are likely to decline.
Production challenges also exist: both HBM memory and wafer supply are highly constrained. OpenAI relies on Broadcom for physical design and Celestica for systems and racks, while maintaining direct cooperation with multiple supply chain vendors to increase production capacity. Richard Ho also revealed that Jalapeno is part of a multi-generation roadmap, with the second-generation product already in deep development and the third generation in early conceptual research.
#6. Industry Observations
As a platform focused on multi-asset comparison and trading experience, MSX also values the potential improvement that underlying compute efficiency can bring to trading infrastructure. Lower inference costs and faster response may drive more responsive market data processing and a better matching experience. The exploration of inference chips by frontier AI companies like OpenAI is worth continued industry attention.
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