NVIDIA H100 80GB vs NVIDIA A100 80GB

Analysis

The NVIDIA H100 80GB succeeds the A100 80GB as NVIDIA's flagship datacenter GPU, moving from Ampere to Hopper architecture two years later. Both cards ship the exact same 80GB capacity, so for local or self-hosted LLM inference this isn't a capacity question at all — it's purely about how much faster the newer silicon reads that memory, and what its FP8 Transformer Engine unlocks that Ampere never had.

Because both cards ship 80GB, they fit the identical set of models — 59 of the 85 open-weight models this site tracks run natively in VRAM on either card at 8k context, on the same quantization ladder. The entire difference is speed: the H100's 3,350 GB/s HBM3 is 64% faster than the A100's 2,039 GB/s HBM2e, and this site's calculator shows that gap landing almost exactly in tokens/sec — Llama 3.3 70B at Q4_K_M decodes at 48.1 tok/s on the H100 versus 29.2 tok/s on the A100, a 65% speedup. The H100 also adds a 4th-generation Transformer Engine with native FP8, letting frameworks like TensorRT-LLM and vLLM serve at half the memory-bandwidth cost per weight versus Ampere's FP16/INT8-only path — a gap the raw bandwidth numbers alone understate for FP8-optimized serving stacks.

Bottom line: If you're renting by the hour or buying new, the H100 is the better card for every LLM workload at the same 80GB ceiling — it's strictly faster with no capacity tradeoff, and its FP8 support is where serving frameworks are investing next. The A100 remains the pragmatic choice on the discounted-cloud and secondhand market: for the same VRAM tier it typically costs meaningfully less to rent or buy than an H100, and 29.2 tok/s on a 70B model is still perfectly usable for many self-hosted or batch workloads. Choose the H100 when raw throughput or FP8 serving matters most; choose the A100 when the 80GB ceiling itself is what you need and the H100 premium doesn't pay for itself at your usage volume.