RTX 4070 SUPER vs Tesla V100 16GB

The V100's superior 900 GB/s HBM2 bandwidth over the RTX 4070 SUPER's 504 GB/s GDDR6X enables larger batch sizes in memory-intensive workloads, reducing data transfer bottlenecks during training. Its FP16 performance of 125 TFLOPS vastly exceeds the 35.5 TFLOPS of the RTX 4070 SUPER, accelerating mixed-precision training where half-precision computations dominate, though FP32 at 15.7 TFLOPS lags behind the balanced 35.5 TFLOPS of the newer GPU for single-precision inference. The RTX 4070 SUPER's matched FP16 and FP32 throughput at 35.5 TFLOPS each supports versatile inference pipelines, benefiting tasks requiring precise FP32 operations without Volta's tensor core specialization. Power efficiency favors the RTX 4070 SUPER with 220 W TDP versus 300 W, lowering operational costs in sustained runs. Overall, the V100 excels in high-bandwidth FP16-heavy scenarios, while the RTX 4070 SUPER offers modern balanced performance.

The RTX 4070 SUPER suits inference-heavy workloads and local setups due to its balanced 35.5 TFLOPS FP16 and FP32 performance alongside a lower 220 W TDP. Developers prioritizing PCIe form factor compatibility and Ada Lovelace features like improved ray tracing for hybrid graphics-compute tasks select it over the older V100. Cost-conscious users without cloud access benefit from its efficiency in Stable Diffusion or fine-tuning smaller models within 12 GB VRAM limits.

The V100 16GB excels in cloud-based training where its 125 TFLOPS FP16 and 900 GB/s bandwidth handle large batch sizes effectively, available from $0.10 per hour. Scenarios demanding more than 12 GB VRAM or NVLink interconnects for multi-GPU scaling favor its 16 GB HBM2 capacity. Legacy scientific computing pipelines optimized for Volta tensor cores continue to leverage its strengths despite the 300 W TDP.