A100 PCIe 80GB vs RTX 4080 SUPER

Memory capacity and bandwidth define primary differences: the A100 PCIe 80GB's 80 GB HBM2e VRAM and 2039 GB/s bandwidth support large batch sizes in training large language models, preventing out-of-memory errors common with the RTX 4080 SUPER's 16 GB GDDR6X and 717 GB/s bandwidth. In real-world terms, this allows the A100 to process models exceeding 16 GB, such as billion-parameter LLMs, without splitting across GPUs.

FP16 performance reaches 312 TFLOPS on the A100, ideal for accelerated AI training and inference using mixed precision, compared to 48.7 TFLOPS on the RTX 4080 SUPER. The A100's FP32 at 19.5 TFLOPS lags behind the 4080 SUPER's 48.7 TFLOPS, making the latter preferable for general-purpose computing or graphics rendering requiring single precision. For deep learning, the A100's tensor core advantage translates to 6.4 times faster FP16 throughput, reducing training epochs significantly.

Power efficiency varies: the A100's 400W TDP suits enterprise cooling, while the 4080 SUPER's 320W enables denser cloud deployments. Bandwidth limitations on the 4080 SUPER constrain large-batch inference, often halving effective throughput versus the A100.

The A100 PCIe 80GB excels in enterprise-scale AI training where models demand over 16 GB VRAM, such as full fine-tuning of 70B-parameter LLMs, leveraging its 80 GB HBM2e and 2039 GB/s bandwidth for maximal batch sizes. Its 312 TFLOPS FP16 performance accelerates multi-GPU clusters via NVLink, ideal for research labs or production pipelines processing terabyte-scale datasets.

The RTX 4080 SUPER suits budget-conscious users for inference on models under 16 GB, like quantized 7B LLMs, with cloud pricing from $0.17/hr averaging $0.32/hr. Its balanced 48.7 TFLOPS FP16 and FP32 performance handles Stable Diffusion or small-scale fine-tuning efficiently at 320W TDP, offering 5-6 times lower cost than the A100's $0.89/hr starting rate.