V100 32GB

The NVIDIA V100 32GB is a Volta-generation data center GPU that was the flagship AI accelerator from 2018 to 2020, before the A100 superseded it. Built on the GV100 die (TSMC 12nm), it delivers 112 TFLOPS of FP16 throughput with 32GB of HBM2 at 900 GB/s bandwidth. It was the first GPU to introduce Tensor Cores for accelerated matrix multiplication, establishing the hardware foundation for modern deep learning training.

The 32GB HBM2 configuration fits models up to approximately 16B parameters at FP16. NVLink 2.0 at 300 GB/s bidirectional enables multi-GPU tensor parallelism, though at lower bandwidth than the A100's NVLink 3.0 (600 GB/s) or H100's NVLink 4.0 (900 GB/s). The V100 lacks BF16 and FP8 hardware support — both introduced in later architectures — which limits its efficiency for modern transformer training compared to A100 and H100.

The V100 32GB is best suited for legacy ML training workloads, HPC applications, and budget-conscious teams that need more than 16GB of VRAM at the lowest possible cost. It remains widely available on AWS (p3 instances) and other providers at significantly lower rates than A100. For new workloads, the A100 is almost always the better choice — but for teams with existing V100-optimized code or tight budgets, it remains a viable option.