How Much GPU Memory Is Needed for LLM Fine-Tuning?
Автор: AppliedAI
Загружено: 2024-11-19
Просмотров: 2258
Описание:
This video provides a detailed analysis of GPU memory requirements for fine-tuning AI models, using a 1B model as an example. It explains the memory consumption of key components—model weights, gradients, and optimizer states—and introduces the concept of full fine-tuning, highlighting its proportional scalability for larger models.
The video also explores parameter-efficient fine-tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) and its variant QLoRA, which significantly reduce memory requirements by focusing on fine-tuning a small subset of parameters or using quantization. Practical considerations, such as multi-GPU setups and optimization frameworks like DeepSpeed, are briefly mentioned to offer a comprehensive overview.
Paper: "LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs"
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