A100 PCIe 80GB vs A100 SXM4 80GB for LLM inference

A100 PCIe 80GB vs A100 SXM4 80GB: verdict

Choosing between an A100 PCIe 80GB and an A100 SXM4 80GB for LLM inference comes down to three questions: how much VRAM do you actually need, how fast does each card move tokens once the model is loaded, and what does it cost to rent either one today. On raw capacity, both cards carry the same 80GB of VRAM — the difference between them lies in bandwidth, compute throughput, and price, not in how many concurrent users or what model size you can fit on a single card. On raw memory bandwidth, which is usually the real bottleneck for token-by-token decoding rather than compute, A100 SXM4 80GB is ahead at 1935 GB/s on the A100 PCIe 80GB versus 2039 GB/s on the A100 SXM4 80GB. On price, A100 PCIe 80GB currently comes out ahead, which is worth weighing against the raw specs if you're optimizing for cost per token rather than peak capability. Neither card is a strictly better choice in every dimension — the right pick depends on whether your deployment is VRAM-bound (a large model, long context, or many concurrent users) or throughput-bound (a smaller model where decode speed dominates unit economics). The spec table above breaks down the full comparison — VRAM, bandwidth, compute throughput at bf16 and fp8, NVLink support, and power draw — and the models table above shows which currently-popular models actually fit on each card.