A research facility is selecting hardware for a new on-premises data center cluster dedicated to training large transformer-based language models. Which of the following NVIDIA GPUs is most suitable for this scale of deep learning training, based on VRAM capacity, memory bandwidth, and interconnect technology?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think about what it takes to train a massive LLM. You need huge VRAM to fit those billions of parameters and intermediate states, and you need blazing-fast GPU-to-GPU connections. The A100 80GB HBM2e is designed specifically for this. It has the capacity, the bandwidth, and supports NVLink (which lets GPUs talk at 600 GB/s). The T4 and L4 are low-power chips built for inference, not training. And the RTX 4090, while great for gaming, is a consumer card with slow PCIe interconnects and no data center support.
Full explanation below image
Full Explanation
For training large language models (LLMs) like transformers, memory capacity and interconnect bandwidth are the key hardware bottlenecks. The NVIDIA A100 with 80GB of HBM2e memory is engineered specifically for scale-out data center training. It features high-speed HBM2e memory providing over 1.5 TB/s of bandwidth, and supports third-generation NVLink, enabling GPUs to communicate at up to 600 GB/s. This is critical for distributed training frameworks (like Megatron-LM or DeepSpeed) that split model parameters across multiple GPUs. - The NVIDIA T4 (B) is a low-power (70W) GPU optimized for AI inference at scale, lacking the memory capacity and bandwidth for training. - The NVIDIA L4 (C) is a mainstream accelerator designed for video processing and inference, not high-end LLM training. - The NVIDIA RTX 4090 (A) is a consumer workstation GPU that lacks NVLink support (meaning inter-GPU scaling is restricted to slower PCIe lanes), uses less bandwidth-efficient GDDR6X memory, and is restricted from enterprise data center deployments by its license terms.