During the training of large models, many people tend to focus on the importance of GPU memory requirements, often overlooking the crucial role of RAM. In fact, RAM's impact on large model training can even exceed that of GPU memory itself, particularly evident in data loading, gradient synchronization, optimizer state storage, and multi-GPU distributed training. Understanding why large model training places higher demands on memory requires analyzing the underlying mechanisms of deep learning training, data flow, and distributed computing architecture.

　　First, large models have an extremely large number of parameters, which a single GPU may not be able to accommodate. Therefore, distributed training is typically employed. During distributed training, CPU memory handles model sharding, gradient caching, and optimizer state storage. Unlike small models, large models not only need to store the weights of each layer but also gradient and momentum information (especially when using optimizers such as Adam and AdamW). These gradients and optimizer states are usually stored in host memory and then transferred to the GPU for updates via PCIe or NVLink. If memory is insufficient, the CPU cannot quickly provide data and gradients, causing the GPU to idle and significantly reducing training efficiency.

　　Secondly, batch data in large model training is typically very large. To ensure training effectiveness, a larger batch size is needed to stabilize gradients and improve convergence speed. This means that each training iteration requires reading massive amounts of training samples from disk or high-speed storage, loading them into memory, preprocessing them, and then sending them to GPU memory. Data preprocessing includes operations such as image enhancement, text encoding, normalization, and tokenization, all of which rely on CPU memory. If memory capacity is insufficient, preprocessing speed becomes a bottleneck, preventing the GPU from continuously operating efficiently and thus limiting overall training throughput.

　　Thirdly, large model training typically employs mixed-precision training (FP16/BF16) and gradient accumulation strategies to reduce GPU memory pressure. While mixed precision can reduce GPU memory usage, it requires additional gradient cache, optimizer state, and gradient accumulation buffer storage in memory, which actually increases memory consumption. During gradient accumulation, CPU memory needs to store gradient information from multiple small batches before synchronizing it to the GPU all at once to ensure the correctness of the training logic. Insufficient memory capacity can lead to gradient accumulation failures or necessitate a reduction in batch size, thus impacting training performance.

　　Distributed training and multi-GPU parallelism are also significant reasons for the increased memory requirements of large model training. Data parallelism and model parallelism require storing large amounts of communication buffers, gradient synchronization caches, and distributed task scheduling information in CPU memory. For example, in data parallelism, the gradients of each GPU need to undergo an All-Reduce operation with other GPUs after training. These operations typically rely on CPU memory to cache temporary data to ensure gradient consistency. In model parallelism, parameter sharding, activation value transfer, and forward and backward propagation caching also consume significant amounts of memory. Clearly, the larger the model, the higher these communication and caching requirements become, leading to a corresponding increase in memory load.

　　Furthermore, the optimizers used in large model training also significantly impact memory requirements. Commonly used Adam and AdamW optimizers require storing first-moment and second-moment estimates for each parameter. If the model has billions of parameters, the optimizer state can reach tens or even hundreds of gigabytes. In contrast, the SGD optimizer consumes less memory but has slower convergence and poorer stability, therefore it is not commonly used in large model training. Insufficient memory forces the training framework to frequently swap optimizer states to disk or external storage, severely reducing training efficiency.

　　Large model training also involves a large amount of temporary computational data, such as intermediate activation values, residual caches, and attention weight matrices. This intermediate data needs to be temporarily stored during forward and backward propagation to calculate gradients. For large models, the amount of this temporary data often far exceeds the GPU memory capacity; therefore, some needs to be stored in CPU memory and then transferred to the GPU via a high-speed bus for computation. The more memory available, the less time the GPU spends waiting for data, resulting in higher training efficiency.

　　Another point to note is that high-resolution, multimodal, or long-sequence training further increases memory requirements. Image generation, video understanding, and long text understanding tasks inherently involve massive amounts of input data. If memory is insufficient, the entire batch of data cannot be loaded smoothly, forcing the GPU to slow down or split the input, thus reducing training efficiency. With the large number of parameters in large models and the large amount of input data, memory bottlenecks become a key limiting factor in the entire training process.

　　In terms of hardware design and resource allocation, it is generally recommended that the memory capacity for training large models be 3-5 times or even higher than the GPU memory. This ensures sufficient space for data preprocessing, gradient caching, optimizer states, multi-GPU communication buffers, and other CPU tasks. Insufficient memory will cause the GPU to frequently wait for CPU calls, slowing down training and even leading to OutOfMemoryError (OOM) errors. On the other hand, while excessive memory may not directly affect training, it increases costs and reduces resource utilization. Therefore, allocating memory after reasonably evaluating model size, training tasks, batch size, and optimization strategies is key to improving the training efficiency of large models.

　　Besides capacity, memory bandwidth and storage speed are equally important. During training, large amounts of data need to flow rapidly between hard drives, RAM, and GPU memory. NVMe SSDs combined with large-capacity memory ensure that data is quickly delivered to the GPU, reducing waiting time. NUMA optimization, multi-channel memory, and high-speed memory channels can further improve the efficiency of data transfer from CPU memory to GPU, thereby improving overall training performance.

　　In summary, the higher memory requirements of AI servers for large model training are determined by a combination of factors: large model parameter count necessitates storage for optimizer states and gradient caches; large training batches require CPU memory for preprocessing and data caching; distributed training requires memory to store communication buffers and synchronization information; mixed precision and gradient accumulation increase temporary memory load; and high resolution, multimodal processing, and long input sequences further increase memory requirements. Clearly, GPU memory is only responsible for computation and model parameter handling, while system memory supports the data flow throughout the training process; the two must be coordinated to maximize training efficiency.

　　Therefore, when configuring AI servers for large model training, the ratio of GPU memory to system memory is not a fixed value but should be dynamically adjusted based on model size, data type, training strategy, optimizer, and multi-GPU strategy. It is generally recommended that system memory be 3-5 times or higher than GPU memory, and equipped with high-speed storage and multi-channel memory to ensure smooth data feeding to the GPU. Proper memory configuration not only improves training speed but also reduces the risk of OutOfMemoryError (OOM), improves server resource utilization, and ensures smooth training of large models.