A100 SXM4 80GB vs MI355X

Compute specifications reveal stark contrasts in precision handling: the A100 achieves 312 TFLOPS in FP16 versus 19.5 TFLOPS in FP32, favoring mixed-precision training where FP16 accelerates matrix operations. The MI355X balances FP16 and FP32 at 2300 TFLOPS each, enabling efficient training and full-precision simulations, while its 4600 TFLOPS FP8 suits quantized inference for faster throughput on large language models.

Memory differences profoundly impact workloads: 288 GB HBM3e on the MI355X versus 80 GB HBM2e on the A100 allows processing models with trillions of parameters without splitting, and 8000 GB/s bandwidth supports batch sizes up to four times larger than the A100's 2039 GB/s limit. This reduces out-of-memory errors in training and enables real-time inference at scale.

Power draw underscores trade-offs: the A100's 400W TDP suits dense clusters, whereas the MI355X's 750W demands advanced cooling but yields superior performance per watt in memory-bound tasks.

The A100 SXM4 80GB excels in scenarios requiring immediate availability and cost efficiency: cloud pricing starts at $0.13 per hour across 32 live offers, averaging $1.24 per hour. Its 400W TDP and NVLink interconnect integrate seamlessly into existing NVIDIA ecosystems for PCIe 4.0 or InfiniBand clusters.

Choose the A100 for production deployments where mature CUDA software support outweighs raw specs, such as fine-tuning models under 80 GB VRAM.

The MI355X dominates in memory-intensive applications: 288 GB HBM3e handles massive datasets and models infeasible on 80 GB setups. Its 8000 GB/s bandwidth and 2300 TFLOPS across FP16 and FP32 accelerate training cycles significantly.

Opt for the MI355X in forward-looking HPC environments leveraging Infinity Fabric, despite higher 750W TDP, for workloads like trillion-parameter LLM training.