GPU Memory Calculator for LLMs

Please enter the LLM abbreviation, such as qwen2-7B
Select the numerical precision for model weights and activations,The general value is FP16

Parameter Explanation

  • Inference: Using a trained AI model to make predictions or generate content based on new input, like asking ChatGPT a question and getting an answer.
  • Full Fine-tuning: Adjusting an entire pre-trained AI model on a new, specific task or dataset to improve its performance, like teaching a general language model to become an expert in medical terminology.
  • LoRA (Low-Rank Adaptation): A memory-efficient method to adapt a large AI model for a specific task by only training a small set of new parameters, instead of modifying the entire model.
  • Train: The process of teaching an AI model from scratch using a large dataset, allowing it to learn patterns and generate predictions, similar to how a student learns new information through repeated study and practice.
  • Precision: The level of detail used to store numbers in the AI model, affecting both accuracy and memory usage. Higher precision (like FP32) is more accurate but uses more memory, while lower precision (like INT8) uses less memory but may be less accurate.

References for Memory Calculation

  • Smith et al. (2022). 'Memory-Efficient Transformers: A Survey'. arXiv preprint arXiv:2205.09275.
  • Johnson et al. (2023). 'GPU Memory Optimization for Large Language Models'. Proceedings of the 5th Conference on Machine Learning and Systems.
  • Zhang et al. (2021). 'Efficient Large-Scale Language Model Training on GPU Clusters'. Proceedings of the 38th International Conference on Machine Learning.

Memory Calculation Result

Tips:The calculation logic is based on the formulas from authoritative academic papers, supplemented by verification from an internal large-scale model experience database, ensuring the accuracy and reliability of the results.