The short answer: the NVIDIA H100 is the faster GPU, often by a wide margin for transformer workloads, because it adds FP8 precision through its Transformer Engine and has a newer architecture. The A100 is a generation older, but it still runs every major AI framework, costs less to buy and to rent, and handles fine-tuning and inference for small and mid-sized models well. Pick the H100 when training speed drives your schedule, and the A100 when monthly cost matters more and your models fit in memory.
A100 vs H100: specifications compared
| Specification | A100 40GB | A100 80GB | H100 80GB |
|---|---|---|---|
| Architecture | Ampere | Ampere | Hopper |
| Launch year | 2020 | 2020 | 2022 |
| Memory | 40 GB HBM2, ECC | 80 GB HBM2e, ECC | 80 GB HBM |
| Memory bandwidth | 1,555 GB/s | 2,039 GB/s | Higher than A100 |
| FP8 support | No | No | Yes (Transformer Engine) |
| Multi-Instance GPU | Up to 7 instances | Up to 7 instances | Up to 7 instances |
| TDP (PCIe version) | 250 W | 300 W | Up to 350 W |
Both GPUs work with CUDA, PyTorch, TensorFlow, vLLM and TensorRT-LLM, so your code does not need to change when you move between them.
Performance differences
The biggest gap comes from precision. The A100 supports FP32, FP16, BF16 and INT8, but not FP8. The H100 adds a Transformer Engine that switches layers between FP8 and higher precision automatically during training and inference. FP8 halves memory use compared with FP16 and raises throughput.
In practice, the H100 trains transformer models noticeably faster than the A100 and serves more requests per GPU at inference time. The advantage is largest when the software stack uses FP8, for example through TensorRT-LLM or recent versions of vLLM. In FP16 or BF16 the H100 is still faster, but the gap is smaller.
Multi-GPU scaling also differs. On HGX and SXM systems, H100 cards talk to each other over NVLink 4 at up to 900 GB/s. PCIe cards of either generation communicate more slowly, which matters less for single-node fine-tuning and inference.
Memory and which models fit
Memory decides which models you can run at all. A useful rule of thumb for model weights:
- FP16 or BF16: about 2 GB of VRAM per billion parameters
- 8-bit: about 1 GB per billion parameters
- 4-bit: about 0.6 GB per billion parameters
Add memory for context and the KV cache, which grows with sequence length and the number of concurrent users. With that in mind:
- A 7B model in FP16 needs roughly 14 GB for weights and runs comfortably on a single A100 40GB.
- A 13B model in FP16 needs about 26 GB, which fits on an A100 40GB with moderate context.
- A 70B model in 4-bit needs around 42 GB, so it needs an 80 GB card or two 40 GB cards with the model split between them.
- A 70B model in FP16 needs about 140 GB and does not fit on a single 80 GB card of either type.
Full fine-tuning needs much more memory than inference, because gradients and optimizer states also live on the GPU. Methods like LoRA and QLoRA reduce this enough to fine-tune mid-sized models on A100 hardware. The H100's FP8 support gives it extra headroom at the same 80 GB.
Cost and availability
The H100 costs more to buy and more to rent per hour or per month. The A100 has been on the market longer, so it is widely available from hosting providers at lower prices. The real question is whether the extra speed saves enough time to justify the price. If a training run finishes overnight on A100s and nobody waits on the result, paying for H100 speed brings little benefit.
When to choose the A100
- You run inference for 7B to 13B models, or quantized larger models.
- You fine-tune with LoRA or QLoRA rather than training from scratch.
- Your budget matters more than time to result.
- You want to split one GPU into isolated instances with MIG for several teams or services.
When to choose the H100
- You train large transformer models and training time directly affects your product schedule.
- You serve high traffic and need maximum throughput per GPU.
- Your software already uses FP8 through the Transformer Engine or TensorRT-LLM.
- You need NVLink 4 across many GPUs for distributed training.
A100 servers at IPHOST
IPHOST offers a 2x NVIDIA A100 dedicated server hosted in our own ISO/IEC 27001 certified data center in Chișinău, Moldova. The server is an HPE DL380 Gen10 with two NVIDIA A100 40GB PCIe cards, two Intel Xeon Gold 6230R processors (52 cores), 256 GB of RAM, two 1.6 TB SAS SSDs and a 1 Gbps port with 30 TB of traffic. It is available on pre-order with delivery in 14 days, billed quarterly, with up to 14% off when you pay for 12 months. Together the cards give you 80 GB of GPU memory.
The server suits private inference and fine-tuning, and it is a solid base for LLM hosting. If your workload is lighter, our GPU server hosting range also includes servers with a single NVIDIA L4 24GB or NVIDIA A40 48GB.
Conclusion
The H100 wins on speed, especially with FP8, and it is the right tool for large-scale training. The A100 remains a capable, cost-efficient GPU for inference and fine-tuning when you do not need the newest generation. Estimate your memory needs and the value of training time, then choose.
