A100 SXM4 80GB vs RTX 4080 SUPER

The A100's 312 TFLOPS FP16 performance vastly outpaces the RTX 4080 SUPER's 48.7 TFLOPS, enabling faster mixed-precision training for large neural networks where tensor core utilization dominates. Its 19.5 TFLOPS FP32 trails the RTX 4080 SUPER's 48.7 TFLOPS, but real-world AI tasks rarely bottleneck on FP32 alone: the A100 excels in training scenarios requiring high throughput. For inference, the RTX 4080 SUPER's balanced FP16/FP32 at 48.7 TFLOPS supports efficient deployment of smaller models.

Memory specs define workload feasibility: the A100's 80 GB HBM2e VRAM and 2039 GB/s bandwidth handle massive batch sizes and models exceeding 16 GB, preventing out-of-memory errors in LLM training. The RTX 4080 SUPER's 16 GB GDDR6X at 717 GB/s limits it to smaller batches or models, reducing throughput for memory-intensive tasks by up to 4x in bandwidth terms. Power efficiency favors the RTX 4080 SUPER at 320W versus 400W, aiding dense cloud deployments.

Choose the A100 SXM4 80GB for large-scale AI training or inference where 80 GB VRAM accommodates models like 70B-parameter LLMs without quantization. Its 2039 GB/s bandwidth supports batch sizes up to 4x larger than the RTX 4080 SUPER's 717 GB/s, accelerating convergence in distributed setups via NVLink and InfiniBand. Cloud users needing 312 TFLOPS FP16 for enterprise HPC prioritize it despite $1.39/hr average cost.

The RTX 4080 SUPER suits cost-sensitive tasks like fine-tuning small models or Stable Diffusion generation, leveraging 48.7 TFLOPS FP16/FP32 at $0.32/hr average. Its 16 GB VRAM handles consumer AI workflows without excess capacity, and 320W TDP enables higher cloud density. Gamers or visualization pros benefit from Ada Lovelace efficiencies over Ampere.