---
title: Training a Model with Limited Memory
url: https://www.dataloco.com/en/training-a-model-with-limited-memory
published: 2026-09-15T14:10:42+00:00
language: en
section: Developers
source: https://machinelearningmastery.com/training-a-model-with-limited-memory-using-mixed-precision-and-gradient-checkpointing/
publisher: Dataloco
---

# Training a Model with Limited Memory

Training a language model presents significant challenges due to high memory requirements, stemming from the model size and the lengthy sequences often found in training data batches. Techniques to facilitate training in memory-constrained settings are essential for developers working with large-scale models. This article explores strategies for training models under these constraints, particularly focusing on mixed precision and gradient checkpointing.

The default data type used in PyTorch is the IEEE 754 32-bit floating-point format, commonly referred to as single precision. While single precision is widely utilized, other floating-point types such as 64-bit double precision and half-precision floating point are available. These alternatives offer different ranges and precisions, influencing model performance and memory usage.

Floating-point numbers function as binary representations of real numbers, comprising a sign bit, exponent bits, and mantissa bits. Their arrangement allows for the retention of numerical order when sorted by binary representation. However, different floating-point types are not universally supported across all hardware, which can complicate training processes. For instance, fp4 is exclusive to Nvidia's Blackwell architecture.

The Google Brain team has identified the limitations of using float16, which can lead to overflow or underflow issues due to its limited dynamic range. They proposed the bfloat16 format, which maintains the same dynamic range as float32 but sacrifices some precision. This trade-off is particularly advantageous in deep learning frameworks, where dynamic range often supersedes precision requirements.

Utilizing PyTorch, developers can easily specify the data type for tensors. By defaulting to bfloat16, memory consumption can be halved, allowing for increased batch sizes during training. For example, a model trained on a GPU with 12GB of VRAM could accommodate a batch size of 16 when using bfloat16, compared to a maximum of 8 with float32.

Implementing automatic mixed precision (AMP) training in PyTorch can further optimize memory usage and training speed. The torch.amp sub-library automatically adjusts the data type based on the mathematical operation being performed, ensuring that operations sensitive to precision are executed in float32 while others can be carried out in lower precision. This approach not only conserves memory but can also enhance training speed significantly.