Felafax successfully fine-tuned the LLaMA 3.1 405B model on AMD MI300x GPUs using JAX, demonstrating impressive performance and scalability. By leveraging JAX's platform-independent optimizations and the device mesh feature for efficient parameter sharding, the team achieved near-linear scaling across 8 GPUs. This endeavor highlights AMD GPUs as a viable alternative to NVIDIA hardware for large-scale AI training, providing higher performance per dollar. The full open-sourced implementation is available on GitHub.
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What is JAX and why we picked itJAX on AMD was a breezy setup!Training LLaMA 405B: Performance and ScalabilityOur Training SetupLoading the Model and Sharding ParametersImplementing LoRA TrainingConclusionSort: