NVIDIA H100 80GB vs NVIDIA A100 80GB
Side-by-side local AI comparison: VRAM, memory bandwidth, model compatibility, and estimated tokens per second across 110 open-weight models.
Quick verdict
NVIDIA H100 80GB is faster for local AI inference, but only on memory bandwidth (64.3% more). Both GPUs have 80 GB VRAM and run the exact same 74 models natively; neither fits anything the other can't.
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: 74 of the 110 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, but single-stream decoding in llama.cpp cannot turn all of that into tokens per second, so this site's calculator shows a smaller gain: Llama 3.3 70B at Q4_K_M decodes at 26.6 tok/s on the H100 versus 21.1 tok/s on the A100, a 26% speedup. Published llama.cpp runs agree the gap is modest (an H100 PCIe at 25.0 tok/s against an A100 PCIe at 22.3 on Llama 3 70B Q4_K_M); batched serving stacks like vLLM and TensorRT-LLM are where more of the bandwidth gap shows up. 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 21.1 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.
Specs comparison
| Spec | NVIDIA H100 80GB | NVIDIA A100 80GB |
|---|---|---|
| VRAM | 80 GB | 80 GB |
| Memory type | HBM3 | HBM2e |
| Bandwidth | 3350 GB/s(+64%) | 2039 GB/s |
| Architecture | Hopper | Ampere |
| Backend | CUDA | CUDA |
| Tier | Datacenter | Datacenter |
| Released | 2022 | 2020 |
| Models (native) | 74 | 74 |
Estimated tokens per second
Computed from memory bandwidth and model active-parameter weight. Assumes model fits natively in VRAM.
| Model | NVIDIA H100 80GB | NVIDIA A100 80GB | Delta |
|---|---|---|---|
| Llama 3.3 70B Instruct(70B) | 20.7 t/s(Q6_K) | 16.3 t/s(Q6_K) | +27% |
| Qwen 3.6 27B(27B) | 23.1 t/s(BF16) | 18.1 t/s(BF16) | +28% |
| Llama 3.1 8B Instruct(8B) | 70 t/s(BF16) | 55.5 t/s(BF16) | +26% |
| Qwen 2.5 7B Instruct(7.6B) | 76.8 t/s(BF16) | 60.8 t/s(BF16) | +26% |
Delta is NVIDIA H100 80GB relative to NVIDIA A100 80GB, compared at the highest quantization that fits natively on both cards.
Only NVIDIA H100 80GB can run(0)
No exclusive models: NVIDIA A100 80GB can run everything NVIDIA H100 80GB can.
Only NVIDIA A100 80GB can run(0)
No exclusive models: NVIDIA H100 80GB can run everything NVIDIA A100 80GB can.
Both run natively(74)
These models fit in VRAM on both GPUs. Bandwidth determines which runs them faster.
- Mixtral 8x22B Instruct v0.159.9 t/svs49.1 t/s
- Mistral Medium 3.5 128B19.2 t/svs15.1 t/s
- Ornith 1.5 122B (MoE)132.2 t/svs115.5 t/s
- Qwen 3.5 122B-A10B (MoE)132.2 t/svs115.5 t/s
- Nemotron 3 Super 120B82.4 t/svs73.5 t/s
- Nemotron 3.5 Super VL 120B-A12B87.4 t/svs78.6 t/s
- GPT-OSS 120B194.2 t/svs172.1 t/s
- Llama 4 Scout 109B69.3 t/svs59.6 t/s
- GLM-4.5 Air 106B98.2 t/svs82.6 t/s
- GLM-4.6V 106B98.2 t/svs82.6 t/s
- Qwen 2.5 72B Instruct20.2 t/svs15.9 t/s
- Llama 3.3 70B Instruct20.7 t/svs16.3 t/s
- DeepSeek R1 Distill Llama 70B20.7 t/svs16.3 t/s
- Llama 3.1 70B Instruct20.7 t/svs16.3 t/s
- Mixtral 8x7B Instruct v0.175 t/svs60.4 t/s
- Flint 40B (MoE)162.9 t/svs147.6 t/s
- +58 more on both
Which should you choose?
- • Faster token generation is the priority
- • You want the newer architecture and longer driver support lifecycle
- • Price or availability decides it: NVIDIA A100 80GB has no VRAM, bandwidth or backend advantage over NVIDIA H100 80GB
Frequently asked questions
- Which is better for local AI, the NVIDIA H100 80GB or NVIDIA A100 80GB?
- For local AI inference, the NVIDIA H100 80GB has the edge. It offers 80 GB VRAM (vs 80 GB) and 3350 GB/s bandwidth (vs 2039 GB/s), letting it run 74 models natively in VRAM vs 74 for its rival.
- How much VRAM does the NVIDIA H100 80GB have vs the NVIDIA A100 80GB?
- The NVIDIA H100 80GB has 80 GB of HBM3 at 3350 GB/s. The NVIDIA A100 80GB has 80 GB of HBM2e at 2039 GB/s. Both GPUs have the same VRAM amount; bandwidth determines which generates tokens faster.
- Can the NVIDIA H100 80GB run Llama 3.3 70B?
- Yes. The NVIDIA H100 80GB runs Llama 3.3 70B natively at Q6_K quantization at approximately 20.7 tokens per second.
- Can the NVIDIA A100 80GB run Llama 3.3 70B?
- Yes. The NVIDIA A100 80GB runs Llama 3.3 70B natively at Q6_K quantization at approximately 16.3 tokens per second.
- What is the difference between the NVIDIA H100 80GB and NVIDIA A100 80GB for AI?
- The key difference for AI inference is VRAM and memory bandwidth. The NVIDIA H100 80GB has 80 GB VRAM at 3350 GB/s (CUDA backend). The NVIDIA A100 80GB has 80 GB VRAM at 2039 GB/s (CUDA backend). VRAM determines which models fit; bandwidth determines tokens per second. The NVIDIA H100 80GB runs 74 models natively vs 74 for the NVIDIA A100 80GB.