--- title: "Memory Consumption During LLM Training" description: "This blog is to provide the basic knowledge how the memory is use in LLMs" author: "Yash Srivastava" published: 2026-06-18 updated: 2026-06-18 canonical: https://answers.mindstick.com/blog/415/memory-consumption-during-llm-training category: "technology" tags: ["llm", "ai model", "memory"] reading_time: 4 minutes --- # Memory Consumption During LLM Training ## Introduction When we train a Large [Language Model](https://www.mindstick.com/interview/34471/what-is-a-large-language-model-llm) ([LLM](https://www.mindstick.com/forum/162047/what-are-large-language-models-llms-and-how-do-they-work)), GPU memory plays a very important role in the [training process](https://www.mindstick.com/forum/157942/what-are-the-common-loss-functions-used-in-deep-learning-how-do-they-impact-the-training-process). Many people think that GPU memory is only used to store the model weights, but actually memory is consumed by several components during training. Modern LLMs such as ChatGPT, LLaMA, Gemini and Claude contains billions of parameters. During training, the model not only stores the weights but also stores intermediate calculations, gradients and optimizer states. Due to this reason training a Large Language Model requires a very large amount of GPU memory. The [memory usage](https://www.mindstick.com/forum/33530/convenience-vs-init-method-memory-usage) of an LLM can be [divided into four](https://answers.mindstick.com/qa/100421/when-pakistan-will-be-divided-into-four-parts) major components: ```plaintext GPU Memory │ ├── Model Weights ├── Activation Memory ├── Gradient Memory └── Optimizer Memory ``` Each component have a different purpose during training and together they consume most of the available GPU memory. Now lets understand each component in detail. ## 1. Model Weights Memory Model Weights are the trainable parameters of a [neural network](https://www.mindstick.com/blog/303767/how-neural-networks-and-new-antibiotics-are-connected). The actual knowledge learned by the model is stored inside these weights. Initially all weights are randomly initialized. *Example:* ```plaintext Weight1 = 0.34 Weight2 = -0.12 Weight3 = 0.98 ``` During training these weights are continuously updated through backpropagation and optimization. When training starts, all model weights are loaded into GPU memory because every layer requires them during the forward pass. *Example:* ```plaintext Input ↓ Weight Matrix ↓ Output ``` Every calculation inside Embedding Layers, Attention Layers and Feed Forward Networks uses model weights. Larger models contain more weights. Example: ```plaintext 7 Billion Parameters ↓ 7 Billion Weights ``` As the number of parameters increases, weight memory also increases. ## 2. Activation Memory Activation Memory is one of the largest consumers of GPU memory during training. During the forward pass every layer generates an output. *Example:* ```plaintext Input ↓ Layer 1 Output ↓ Layer 2 Output ↓ Layer 3 Output ↓ Prediction ``` These outputs are called activations. A common misunderstanding is that after a layer produces an output, it can be removed from memory. However this is not [possible because](https://answers.mindstick.com/qa/39472/the-invention-of-swarm-intelligence-has-been-possible-because-of-which-branche-of-science) backpropagation requires these activations later for gradient calculation. For this reason activations are stored in GPU memory until the backward pass is completed. *Example:* ```plaintext Layer 1 → Activation 1 Layer 2 → Activation 2 Layer 3 → Activation 3 ``` The larger the batch size and context length, the larger the activation memory becomes. In many cases activation memory consume more memory than model weights. ## 3. Gradient Memory After the model generates predictions, the prediction is compared with the actual answer using a loss function. *Example:* ```plaintext Prediction: Paris = 20% Actual: Paris ``` After calculating the loss, backpropagation computes gradients for every trainable weight. *Example:* ```plaintext Weight A Gradient = +0.03 Weight B Gradient = -0.07 Weight C Gradient = +0.01 ``` Gradients tells the optimizer: - Which direction the weight should move - How much the weight should change These gradients must be stored in memory until the optimizer updates the weights. Since every trainable parameter requires a gradient, gradient memory can become very large for billion-parameter models. ## 4. Optimizer Memory Modern LLMs commonly use AdamW optimizer. The optimizer does not only store the current gradients. It also stores [additional information](https://www.mindstick.com/forum/160484/error-failed-to-launch-debug-adapter-additional-information-may-be-available-in-the-output-window) about previous updates. For every parameter AdamW typically stores: ```plaintext Weight Gradient Momentum Variance ``` Momentum helps the optimizer remember previous learning direction. Variance helps the optimizer adjust the update size. Because of these additional values, optimizer memory can consume even more memory than the model weights themselves. This is [one of the major](https://yourviews.mindstick.com/view/109/gender-inequality-still-one-of-the-major-flaw-in-our-society) reasons why training requires much more memory than inference. ## Why Long Context Length Increases Memory? Modern LLMs support long context windows such as: ```plaintext 4K Tokens 8K Tokens 32K Tokens 128K Tokens ``` As the number of tokens increases, attention calculations increase significantly. Attention Complexity: This means that increasing context length increases activation memory and attention memory dramatically. Because of this reason long-context training is very expensive. ## Why Batch Size Increases Memory? Batch Size represents how many training examples are processed at the same time. *Example:* ```plaintext Batch Size = 1 ``` Only one example is stored. ```plaintext Batch Size = 64 ``` Sixty-four examples are stored simultaneously. Larger batch sizes create more activations and therefore consume more GPU memory. ## Memory Breakdown During Training A simplified memory distribution during [LLM training](https://answers.mindstick.com/blog/396/understanding-the-llm-training-flow-from-batch-loading-to-weight-updates) can be represented as: ```plaintext GPU Memory │ ├── Model Weights ├── Activations ├── Gradients └── Optimizer States ``` All four components are required for successful training. If GPU memory becomes insufficient, training may fail due to Out Of Memory (OOM) errors. --- Original Source: https://answers.mindstick.com/blog/415/memory-consumption-during-llm-training Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.