Revolutionary Memory Technology: Guide to Running 70B Parameter LLMs on 4GB GPUs
A groundbreaking development is occurring in artificial intelligence. Thanks to layer-wise inference and advanced memory optimization techniques, 70-billion-parameter large language models can now run on consumer-grade graphics processors with only 4GB of VRAM. This technology overcomes the high-cost hardware barrier, fundamentally transforming accessibility.
A Historic Milestone in AI Accessibility
Despite the dizzying progress recently achieved in artificial intelligence and large language models (LLMs), the field appeared inaccessible to the masses due to high hardware costs. However, newly developed revolutionary memory technologies are fundamentally changing this picture. This method, called layer-wise inference and combined with memory optimization techniques, makes it possible to run models with a massive size of 70 billion parameters on consumer-grade GPUs with only 4GB of video memory (VRAM). This development is literally creating a revolution in the field.
The Revolutionary Method Behind the Technology: Layer-wise Inference
In traditional methods, an entire large language model would be loaded into GPU memory, which could require hundreds of gigabytes of memory. The new approach is based on the principle of running the model in parts. In the layer-wise inference technique, the model's layers are processed sequentially. After one layer's computation is completed, the necessary data for the next layer is loaded, thereby drastically reducing the amount of data that needs to be held in memory simultaneously. This can be thought of like reading a massive book one page at a time, holding that page in memory before moving to the next, instead of trying to read the entire book at once. This segmented processing offers the possibility to run giant models even on low-memory or older-generation cards like NVIDIA's GTX 1650 or RTX 3050.
Memory Optimization and Democratizing AI
When combined with advanced memory management algorithms, the layer-wise inference technique provides incredible efficiency. Optimizations such as compressing model weights, dynamic memory allocation, and preventing unnecessary data copying maximize resource utilization. This situation removes the barriers in front of artificial intelligence development and research, democratizing access. It enables students, independent developers, and small-scale researchers to experiment with and develop state-of-the-art models without requiring data center-level investments. This technological leap is not just a performance improvement; it represents a significant step towards making AI a universal tool.
