A100 PCIe 80GB vs H200 SXM

Memory differences dominate real-world impacts: the H200's 141 GB HBM3e VRAM exceeds the A100's 80 GB HBM2e, supporting models up to 175 billion parameters without sharding, whereas the A100 requires multi-GPU setups for such scales. Bandwidth at 4800 GB/s on the H200 versus 2039 GB/s on the A100 permits larger batch sizes in training, reducing iterations by up to 2.4 times for memory-bound tasks.

Compute specs favor the H200 across precisions. FP16 performance of 1979 TFLOPS dwarfs the A100's 312 TFLOPS, accelerating transformer training by over 6 times. FP32 at 67 TFLOPS versus 19.5 TFLOPS benefits scientific simulations, while FP8 at 3958 TFLOPS optimizes inference for quantized LLMs. Higher TDP of 700W on the H200 sustains these rates, though it demands robust cooling.

Interconnect upgrades to PCIe 5.0 and NVLink on the H200 enhance multi-node scaling over the A100's PCIe 4.0, reducing latency in distributed training by 20-50 percent depending on topology.

The A100 PCIe 80GB suits cost-sensitive deployments. At $0.89 per hour starting price and 400W TDP, it fits power-constrained environments or budgets where 80 GB VRAM handles models up to 70 billion parameters. PCIe form factor simplifies integration into existing servers without SXM infrastructure.

Legacy workloads optimized for Ampere excel here: 312 TFLOPS FP16 supports fine-tuning or inference on mid-scale LLMs without overprovisioning.

The H200 SXM excels in memory-intensive AI tasks. 141 GB VRAM accommodates full 175 billion parameter models in single-GPU inference, avoiding tensor parallelism overheads present on the A100's 80 GB limit. 4800 GB/s bandwidth doubles effective throughput for large-batch training.

High-precision demands favor its 1979 TFLOPS FP16 and 3958 TFLOPS FP8, ideal for cutting-edge LLM development despite 700W TDP and $1.19 per hour starting cost.