A100 SXM4 40GB vs RTX 5080

The A100 demonstrates superior FP16 throughput at 312 TFLOPS compared to the RTX 5080's 56.3 TFLOPS: this gap accelerates mixed-precision neural network training, where tensor cores on A100 process AI workloads five and a half times faster. RTX 5080 balances FP16 and FP32 at 56.3 TFLOPS each, which benefits graphics rendering and general-purpose computing requiring scalar operations.

Memory specifications highlight a key divide: A100's 40 GB HBM2e and 2039 GB/s bandwidth enable larger batch sizes in model training, reducing overhead for large language models that exceed 16 GB GDDR7 on RTX 5080. Lower bandwidth of 960 GB/s on RTX 5080 limits throughput for memory-bound inference, though its 360 W TDP offers slight efficiency over A100's 400 W.

Real-world implications favor A100 in multi-GPU clusters via NVLink and PCIe 4.0, while RTX 5080 suits single-node tasks on PCIe with newer Blackwell optimizations.

Select the A100 SXM4 40GB for workloads demanding high memory capacity: its 40 GB HBM2e VRAM accommodates large models without partitioning, unlike the RTX 5080's 16 GB limit. The 2039 GB/s bandwidth supports massive batch sizes in training, and NVLink interconnect enables efficient scaling across multiple GPUs.

Datacenter environments benefit from A100's 312 TFLOPS FP16 performance, ideal for enterprise AI pipelines where PCIe 4.0 and InfiniBand provide robust connectivity.

Opt for the RTX 5080 in cost-sensitive scenarios: cloud pricing starts at $0.25 per hour averaging $0.38 per hour, far below A100's $1.00 per hour average of $2.80 per hour. Its 360 W TDP consumes less power than A100's 400 W, suiting edge or small-scale deployments.

Gaming, creative applications, and lighter AI inference leverage the balanced 56.3 TFLOPS FP16 and FP32 on Blackwell architecture, with 16 GB GDDR7 sufficient for models under that threshold.