What hardware is required to train an LLM?

To train a large language model (LLM), you need three core hardware components: high-performance GPUs, sufficient memory and interconnect solutions, and scalable storage. The primary requirement is computational power, which comes from modern GPUs designed for parallel processing. NVIDIA’s A100 or H100 GPUs are common choices due to their tensor cores and high memory bandwidth, which accelerate matrix operations critical for training neural networks. For example, a single A100 GPU provides up to 312 teraflops of performance and 40-80GB of memory, but even this is insufficient for larger models, requiring multiple GPUs working in parallel.

Memory capacity and interconnect speed are equally important. LLMs with billions of parameters demand significant GPU memory (VRAM) to store model weights and intermediate computations. For instance, training a 175B-parameter model like GPT-3 might require dozens of GPUs with hundreds of gigabytes of combined VRAM. To connect these GPUs efficiently, technologies like NVIDIA’s NVLink (enabling 600GB/s bandwidth between GPUs) or high-speed inter-node networking (e.g., InfiniBand at 400Gbps) are essential. Without fast interconnects, communication bottlenecks between GPUs can drastically slow training. Distributed training frameworks like PyTorch’s FSDP or TensorFlow’s MultiWorkerMirroredStrategy rely on these technologies to scale across hardware.

Finally, storage and infrastructure play a critical role. Training data for LLMs often involves terabytes of text, requiring fast storage (e.g., NVMe SSDs) to load data without bottlenecking the GPUs. Checkpointing—saving model states during training—also demands large, reliable storage (e.g., distributed file systems or cloud storage) to handle multi-gigabyte snapshots. Additionally, power and cooling must be addressed: a cluster of 8 A100 GPUs can draw over 5kW, necessitating robust cooling systems and redundant power supplies. For example, cloud providers like AWS or Azure offer preconfigured instances (e.g., AWS P4d) that bundle these components, simplifying setup for developers.