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Topic: "custom-accelerators"
OpenAI Titan XPU: 10GW of self-designed chips with Broadcom
llama-3-70b openai nvidia amd broadcom inferencemax asic inference compute-infrastructure chip-design fp8 reinforcement-learning ambient-agents custom-accelerators energy-consumption podcast gdb
OpenAI is finalizing a custom ASIC chip design to deploy 10GW of inference compute, complementing existing deals with NVIDIA (10GW) and AMD (6GW). This marks a significant scale-up from OpenAI's current 2GW compute, aiming for a roadmap of 250GW total, which is half the energy consumption of the US. Greg from OpenAI highlights the shift of ChatGPT from interactive use to always-on ambient agents requiring massive compute, emphasizing the challenge of building chips for billions of users. The in-house ASIC effort was driven by the need for tailored designs after limited success influencing external chip startups. Broadcom's stock surged 10% on the news. Additionally, InferenceMAX reports improved ROCm stability and nuanced performance comparisons between AMD MI300X and NVIDIA H100/H200 on llama-3-70b FP8 workloads, with RL training infrastructure updates noted.