RTX 4080 SUPER vs Tesla V100 32GB

Key spec differences translate directly to workload impacts: the V100's 125 TFLOPS FP16 vastly exceeds the RTX 4080 SUPER's 48.7 TFLOPS, accelerating mixed-precision training where FP16 dominates, but its 15.7 TFLOPS FP32 lags behind the RTX 4080 SUPER's 48.7 TFLOPS, limiting single-precision inference or simulations. Memory bandwidth plays a critical role in batch sizes: the V100's 900 GB/s HBM2 supports larger batches for training massive models compared to the RTX 4080 SUPER's 717 GB/s GDDR6X, reducing data transfer bottlenecks in memory-intensive operations. The V100's 32 GB VRAM enables handling larger models without swapping, whereas the RTX 4080 SUPER's 16 GB constrains it to smaller batches or models. Power draw remains close at 300W for V100 and 320W for RTX 4080 SUPER, but the V100's SXM2 and NVLink options facilitate multi-GPU scaling for distributed training, outperforming the RTX 4080 SUPER's PCIe-only setup in clustered environments.

Opt for the RTX 4080 SUPER in cost-sensitive inference pipelines or fine-tuning where balanced 48.7 TFLOPS FP32 and FP16 performance matters, especially at $0.17 per hour starting price versus the V100's $0.29 minimum. Its Ada Lovelace architecture excels in modern frameworks optimized for newer tensor cores, making it ideal for single-GPU deployments in Stable Diffusion or lightweight LLM inference without needing 32 GB VRAM.

Select the V100 32GB for legacy deep learning training leveraging its 125 TFLOPS FP16 and 900 GB/s bandwidth, particularly in multi-GPU setups via NVLink where 32 GB HBM2 handles large batch sizes for models exceeding 16 GB. It remains relevant despite higher $1.01 per hour average cost for workloads like scientific computing requiring high memory capacity and Volta-specific optimizations.