GPU-Memory

When GPT-4 was released in 2023, rumors suggested it contained over 1.7 trillion parameters. Training such a model requires approximately 25,000 A100 GPUs running for months—a feat that would be impossible without sophisticated distributed training systems. The challenge isn’t merely computational; it’s fundamentally a memory problem. A single 80GB A100 GPU can barely hold a 40B parameter model during training, let alone a trillion-parameter behemoth. This is the story of how systems researchers cracked the memory wall through a decade of innovations in data parallelism, ZeRO, tensor parallelism, and pipeline parallelism. ...

In March 2024, a team of researchers attempted to deploy a 70-billion parameter language model on a single NVIDIA H100 GPU with 80GB of VRAM. The model weights alone consumed approximately 140GB in FP16—already exceeding their hardware capacity. But even after applying 4-bit quantization to squeeze the weights down to ~40GB, the system still ran out of memory when processing contexts beyond 8,000 tokens. The culprit wasn’t the model size. It was something far more insidious: the KV cache. ...