Software Meets Hardware: Enabling AMD for Large Language Models

Sharon Zhou and Greg Diamos on Lamini’s pioneering work with AMD in Generative AI.

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Sharon Zhou and Greg Diamos are co-founders of Lamini1, a startup at the forefront of enabling enterprise adoption of large language models (LLMs). We discussed Lamini’s work with AMD, which focused on closing the gap between AMD hardware capabilities and software integration in LLM applications.

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Despite the perception that AMD’s software stack for machine learning was only 10% complete, Lamini recognized it was actually around 90% complete and set out to fill in the remaining 10%. This involved overcoming challenges in various components of the software stack, such as matrix operations and framework compatibility. Their efforts culminated in successfully running a full LLM application on AMD hardware, including tasks like pre-training, fine-tuning, and inference. This achievement not only demonstrated the viability of AMD’s software and hardware for machine learning applications but also contributed to reducing the dominance of other players in the field, ultimately making powerful computing more accessible and affordable for a broader range of users. Lamini’s approach can be likened to creating a CUDA-like layer for AMD, significantly enhancing AMD’s usability in the AI and machine learning domains.

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[1] Ben Lorica is an investor/advisor in Lamini and other startups.