How much GPU memory do you need to fine-tune a model? In this blog, we break down the memory requirements for full fine-tuning and parameter-efficient methods like LoRA and QLoRA, helping you plan your AI projects effectively.
We’ll cover:
Memory Requirements for Full Fine-Tuning
Using a 1 billion parameter model, we explain how memory usage is distributed across model weights, gradients, and optimizer states. You’ll discover why a 1B model requires 12 GB of memory and how this scales for larger models (e.g., a 7B model needs 84 GB).
Efficient Fine-Tuning Techniques with LoRA
Learn how LoRA (Low-Rank Adaptation) drastically reduces memory requirements by fine-tuning only a small subset of parameters. For a 1B model, LoRA can lower GPU memory usage to approximately 2.3 GB.
Even Greater Savings with QLoRA
Discover how QLoRA uses quantization techniques to shrink memory requirements further, making fine-tuning accessible even on resource-limited GPUs.
Real-World Considerations
Explore how factors like single vs. multi-GPU setups, distributed training, and optimization frameworks like DeepSpeed impact memory planning.
For detailed information, please watch our YouTube video: How Much GPU Memory Is Needed for LLM Fine-Tuning?
