A100 PCIe 40GB vs RTX 4080 SUPER

The A100's FP16 performance of 312 TFLOPS vastly outpaces the RTX 4080 SUPER's 48.7 TFLOPS, making it ideal for deep learning training where tensor core operations dominate. In contrast, the RTX 4080 SUPER's equal 48.7 TFLOPS across FP16 and FP32 supports balanced workloads like inference or graphics rendering, where the A100's mere 19.5 TFLOPS FP32 limits versatility outside AI-specific tasks.

Memory specifications drive real-world impacts: the A100's 2039 GB/s bandwidth and 40 GB VRAM allow batch sizes up to 4x larger than the RTX 4080 SUPER's 16 GB and 717 GB/s in memory-bound training scenarios. This enables faster convergence in large models without gradient accumulation hacks required on the consumer GPU.

Power draw differences are notable, with the A100 at 400W TDP versus 320W, implying higher infrastructure costs but sustained performance in multi-GPU clusters via NVLink, absent on the PCIe-only RTX 4080 SUPER.

Choose the A100 PCIe 40GB for large-scale AI training requiring over 16 GB VRAM, such as LLMs with billions of parameters. Its 2039 GB/s bandwidth supports massive batch sizes, reducing training time by enabling efficient data throughput unavailable on the RTX 4080 SUPER.

Enterprise environments benefit from the A100's NVLink interconnect and InfiniBand support, facilitating multi-GPU scaling for scientific computing workloads exceeding the RTX 4080 SUPER's PCIe limitations.

The RTX 4080 SUPER excels in cost-sensitive inference or fine-tuning of models fitting within 16 GB VRAM, offering 48.7 TFLOPS FP32 at $0.17 per hour starting price. Its Ada Lovelace architecture provides modern features like improved ray tracing for hybrid graphics-AI tasks.

Developers prototyping or running Stable Diffusion benefit from the lower 320W TDP and average $0.32 per hour pricing, avoiding the A100's $1.85 per hour overhead for sub-40 GB needs.