Large language models (LLMs) are optimized for memory usage through architectural design, quantization, and memory-efficient training strategies. These optimizations aim to reduce the computational resources required to store and run the models without significantly compromising performance. Developers focus on techniques that lower memory consumption during both training and inference, enabling LLMs to operate on hardware with limited resources.
One key approach involves model architecture adjustments. For example, sparse attention mechanisms reduce memory by limiting the number of tokens the model processes at once. Models like GPT-3 use windowed attention, where each token only interacts with a subset of nearby tokens instead of the entire sequence. Another technique is parameter sharing, where layers reuse weights instead of storing separate parameters. ALBERT, a variant of BERT, employs cross-layer parameter sharing to cut memory usage by up to 90%. These design choices lower the memory footprint while maintaining model capabilities.
Quantization and pruning further optimize memory. Quantization converts model weights from high-precision formats (like 32-bit floats) to lower-precision formats (like 8-bit integers). For instance, GPTQ and QLoRA are methods that quantize LLM weights to 4 bits, reducing memory usage by 75% with minimal accuracy loss. Pruning removes redundant or less important weights from the model. Tools like TensorFlow Lite apply magnitude-based pruning, eliminating near-zero weights. During training, gradient checkpointing saves memory by recomputing intermediate activations during backpropagation instead of storing them. Frameworks like PyTorch’s checkpoint API implement this, trading computation time for memory savings. Together, these techniques allow developers to deploy LLMs on devices with constrained memory, such as mobile phones or edge devices.