A100 SXM4 80GB vs V100 SXM2 32GB for LLM inference

A100 SXM4 80GB vs V100 SXM2 32GB: verdict

A100 SXM4 80GB vs V100 SXM2 32GB: the short version is that A100 SXM4 80GB wins on VRAM (80GB on the A100 SXM4 80GB vs 32GB on the V100 SXM2 32GB), A100 SXM4 80GB wins on raw memory bandwidth (2039 GB/s on the A100 SXM4 80GB vs 900 GB/s on the V100 SXM2 32GB, the number that dominates decode throughput), and live pricing for a direct comparison wasn't fresh enough today, so the price block above shows catalog estimates instead. Which of those matters more depends entirely on your workload: a large model or long-context deployment cares most about the VRAM number, while a smaller model serving many short requests cares much more about bandwidth and cost per token. When VRAM is tied, bandwidth and price become the primary levers. Neither the A100 SXM4 80GB nor the V100 SXM2 32GB is a universally correct answer — they're built for overlapping but distinct points on the capacity-versus-throughput curve, and the gap between them (in bandwidth and price, and potentially VRAM) is usually the whole reason to pick one over the other rather than defaulting to whichever one you already have access to. The full spec table above covers bf16/fp8 compute throughput, NVLink support, and power draw for both cards, and the models table above shows which currently-popular open-weight models actually fit on each one — the more concrete way to answer 'which GPU do I need' than specs alone.