A100 vs RTX 4080

A100 dominates FP16 workloads at 312 TFLOPS: this enables faster model training with mixed precision, where tensor cores accelerate matrix multiplications common in deep learning. RTX 4080 trails at 48.7 TFLOPS FP16 but leads in FP32 at 48.7 TFLOPS versus A100's 19.5 TFLOPS, benefiting simulations or graphics rendering that rely on single-precision floats. The FP16-to-FP32 ratio highlights A100's training focus, while RTX 4080 balances inference and general compute.

Memory differences shape real-world use profoundly. A100's 40-80 GB HBM2e and 2039 GB/s bandwidth support massive batch sizes in large language models, reducing out-of-memory errors during training. RTX 4080's 16 GB GDDR6X and 717 GB/s limit it to smaller models or lower batches, potentially slowing throughput by factors tied to data movement bottlenecks. A100's 400W TDP demands robust cooling, compared to RTX 4080's efficient 320W.

Interconnect advantages favor A100 for clusters: NVLink enables high-speed GPU-to-GPU communication, ideal for distributed training across multiple nodes.

Choose A100 for large-scale model training and inference requiring over 16 GB VRAM. Its 40-80 GB HBM2e handles billion-parameter LLMs with batch sizes infeasible on RTX 4080's 16 GB. NVLink and InfiniBand support multi-GPU setups, scaling performance linearly in HPC environments.

Datacenter deployments favor A100's 2039 GB/s bandwidth: it sustains high throughput for memory-intensive tasks like scientific simulations.

RTX 4080 suits budget-conscious users with smaller workloads. At $0.11 per hour minimum versus A100's $0.45, it delivers value for fine-tuning mid-sized models within 16 GB VRAM limits. Balanced 48.7 TFLOPS across FP16 and FP32 accelerates inference on consumer setups.

Single-GPU tasks benefit from RTX 4080's PCIe form factor and 320W TDP: it fits standard workstations without datacenter infrastructure.