RTX 5080 vs V100

The V100's 125 TFLOPS FP16 significantly outpaces the RTX 5080's 56.3 TFLOPS, enabling faster mixed-precision training for large language models where half-precision dominates. This advantage stems from Volta's tensor core optimizations, allowing larger effective batch sizes despite the 900 GB/s bandwidth. In contrast, the RTX 5080's FP32 performance at 56.3 TFLOPS triples the V100's 15.7 TFLOPS, benefiting inference and tasks requiring full-precision arithmetic.

Memory bandwidth plays a critical role: the RTX 5080's 960 GB/s supports higher throughput for data-intensive operations compared to the V100's 900 GB/s, accommodating bigger batch sizes in inference pipelines and reducing latency. Power consumption differs with the RTX 5080 at 360W TDP versus the V100's 300W, potentially increasing operational costs in dense cloud environments.

Overall, these specs position the V100 for FP16-heavy training and the RTX 5080 for balanced or FP32-centric workloads, with interconnects like NVLink on V100 aiding multi-GPU scaling over the RTX 5080's PCIe.

The RTX 5080 excels in scenarios demanding balanced FP16 and FP32 performance, such as Stable Diffusion generation or LLM inference, where its 56.3 TFLOPS across both precisions outperforms the V100's imbalanced 125 TFLOPS FP16 and 15.7 TFLOPS FP32. Its higher 960 GB/s bandwidth handles larger models efficiently.

Cloud users benefit from the RTX 5080's lower average pricing of $0.38 per hour versus the V100's $0.94, especially with Blackwell architecture efficiencies reducing long-term compute needs.

Opt for the V100 in FP16-dominated workloads like LLM training, leveraging its 125 TFLOPS to accelerate mixed-precision computations far beyond the RTX 5080's 56.3 TFLOPS. NVLink interconnect supports seamless multi-GPU setups unavailable on the PCIe-only RTX 5080.

High availability across 72 offers at a low entry price of $0.10 per hour makes the V100 ideal for budget-sensitive, high-volume training runs despite the higher average of $0.94.