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NVIDIA researchers, in collaboration with the University of Texas at Austin, have introduced Flextron, a novel network architecture and post-training model optimization framework. Flextron, presented as an oral presentation at ICML 2024, allows for the training of a single model that can be optimized for various GPUs at inference without the need for additional retraining. This flexible AI model deployment is achieved with only 5% post-training finetuning and 5% pre training tokens, pushing the boundaries of adaptive and heterogeneous conditional compute. The model supports flexible AI model deployment, showcasing significant advancements in language models and model optimization.
NVIDIA Researchers Introduce Flextron: A Network Architecture and Post-Training Model Optimization Framework Supporting Flexible AI Model Deployment https://t.co/JaZuDNUcwz #AI #Flextron #ModelOptimization #LanguageModels #Adaptability #ai #news #llm #ml #research #ainews #in… https://t.co/SHxpHhsbGK
NVIDIA Researchers Introduce Flextron: A Network Architecture and Post-Training Model Optimization Framework Supporting Flexible AI Model Deployment Researchers from NVIDIA and the University of Texas at Austin introduced FLEXTRON, a novel flexible model architecture and… https://t.co/rrtltouoQG
🚨Flextron: Many-in-One Flexible Large Language Model [ICML'24 Oral] 🚀𝐀𝐛𝐬: https://t.co/87qKjhQuVK Train one model and get many optimal models for each GPU at inference without any additional retraining. https://t.co/mXHPifeZUa
