How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip spectrum.ieee.org/llms-for-c…

There is a lot of deserved hype for how great OpenAI's Jalapeno ASIC and system will be. However, there is one major flaw that no one is talking about at all... (1/6)🧵

SemiAnalysis' Jordan Nanos reveals the OpenAI engineers scrolling their own kernel code had no idea what it did line by line, and it didn't matter because the AI does "It's not slop, because it performs, but it's literally just AI-generated assembly basically puked out in Gluon, this low-level kernel programming language that they built on top of Triton, which uses this really interesting programming model." "The point is, the guys who were scrolling through this code with us, it was kind of clear that they know a whole bunch about hardware, they know a whole bunch about the concepts in the system, and they just have no idea what this MLA kernel that they're showing us for DeepSeek actually does." "You can go line by line and it's like nope, nope, nope." "But it doesn't matter. The AI understands it, the AI tests it, and you see the results. It produces correct kernels that perform really well." "And I think this is just a sign of what's to come again." "That actual code is not necessarily something that a human has to reason about deeply, if the AI knows how to manipulate the data movement and the processing elements on the hardware that you've given it." ________ More takeaways from OpenAI's Jalapeno development: firesidealpha.substack.com/8…

I love the taste of jalapeños. Here's my take on the @OpenAI presentation chip that claims to be faster than @nvidia. piped.video/Ic0kYWjffjI

🌶️ @OpenAI

OpenAI decided to keep the chip team small and use AI for a an improvement loop Early stages of Hardware in the loop RSI?

OpenAI: "Today, we shared the first measured performance results from Jalapeño, OpenAI’s first custom inference chip. On InferenceX, a public benchmark using GPT‑OSS 120B, Jalapeño delivered more peak throughput per kilowatt and lower token latency than the commercial systems in the comparison. It also performed strongly on DeepSeek R1 and Kimi K2, showing that its gains extend across model families." "This is Jevons paradox: greater efficiency makes more uses worthwhile, expanding consumption and creating new economic activity through more work completed, better decisions, more products launched, and more revenue generated." "AI played a direct role in Jalapeño’s development, enabling the team to move from initial design to tapeout in nine months by exploring implementations, shortening design, measurement, and verification loops, and continuously iterating on model workloads. AI also helped optimize the chip’s arithmetic circuits, allowing the team to fit more compute performance into the chip on schedule." "We plan to begin deploying Jalapeño within OpenAI’s compute infrastructure by the end of the year. It is the first generation of a multigenerational roadmap: Gen 2 is deep in development, and Gen 3 is taking shape. Each generation will build on what we learn and further advance both efficiency and speed." "As we prepare for deployment, we are continuing production qualification, maturing the software, preparing to operate Jalapeño at scale, and validating performance across more models."

OpenAI Jalapeño: Better Than Nvidia Blackwell OpenAI’s self-designed ASIC compared with Rubin, Jalapeño’s TCO, throughput per MW, and spicy deets newsletter.semianalysis.com/…

OpenAI Jalapeño: Better Than Nvidia Blackwell OpenAI’s self-designed ASIC compared with Rubin, Jalapeño’s TCO, throughput per MW, and spicy deets newsletter.semianalysis.com/…