NVIDIA · Hopper
H100 SXM 80 GB rental prices and specifications
The 80 GB SXM H100 variant used in HGX systems, with high-bandwidth HBM3 and NVLink connectivity for multi-GPU workloads.
Profile checked · Primary source ↗
- Memory
- 80 GB
- Bandwidth
- 3350 GB/s
- Current offers
- 0
- FP16 dense
- 989.5 TFLOPS
- BF16 dense
- 989.5 TFLOPS
- FP8 dense
- 1979 TFLOPS
- Interconnect
- NVLink up to 900 GB/s; PCIe Gen5 128 GB/s.
Compare H100 SXM 80 GB cloud rentals
Use the exact 80 GB GPU profile when comparing rental quotes. Tables separate currencies, billing periods and rental terms; the required GPU count determines the whole-instance compute cost. Storage, transfer, taxes and other charges can be additional.
Source checks show when each price was actually observed. Historical quotes remain available separately and do not enter current price comparisons. Explore the sourced GPU rental price index or check a rental against your model requirements.
Dense tensor peaks are derived as one half of the manufacturer table values explicitly marked with sparsity. Vendor rounding is retained; these are theoretical peaks, not inference measurements. · Specification source 1 ↗
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Current rental offers
| GPU / provider | Instance price / offer | Terms / location |
|---|---|---|
| No offers match these filters. Try another billing period or currency. | ||
Previously observed offers
227 retained quotes with an old check, a different allocation, or unavailable status. These are excluded from current prices and capacity counts.
Show previously observed offers →Measured performance
Inference benchmarks
| Model | GPU setup | Engine | Quant. | Workload | Reported tok/s | Evidence | Max-load capacity scenarioUSD / million output tokens |
|---|---|---|---|---|---|---|---|
| Llama 3.1 405B Instruct | H100 SXM 80 GB8 GPUs | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.1-405B-Instruct-FP8 | 128 → 2048input → output | 4517.4 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.1 405B Instruct | H100 SXM 80 GB8 GPUs | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.1-405B-Instruct-FP8 | 1000 → 1000input → output | 2955.5 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.1 405B Instruct | H100 SXM 80 GB8 GPUs | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.1-405B-Instruct-FP8 | 2048 → 128input → output | 433.5 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.1 8B Instruct | H100 SXM 80 GB1 GPU | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.1-8B-Instruct-FP8 | 128 → 2048input → output | 21413.2 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.1 8B Instruct | H100 SXM 80 GB1 GPU | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.1-8B-Instruct-FP8 | 1000 → 1000input → output | 14991.6 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.1 8B Instruct | H100 SXM 80 GB1 GPU | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.1-8B-Instruct-FP8 | 2048 → 128input → output | 3275.6 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.3 70B Instruct | H100 SXM 80 GB2 GPUs | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.3-70B-Instruct-FP8 | 128 → 2048input → output | 5892.9 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.3 70B Instruct | H100 SXM 80 GB2 GPUs | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.3-70B-Instruct-FP8 | 1000 → 1000input → output | 4181.1 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
| Llama 3.3 70B Instruct | H100 SXM 80 GB2 GPUs | TensorRT-LLM PyTorchv0.21; methodology pinned at 0c9430e5a530ba958fc9dca561a3ad865ad9f492 | FP8nvidia/Llama-3.3-70B-Instruct-FP8 | 2048 → 128input → output | 723.4 | Source ↗Published Sep 15, 2025 | —No recent non-reference on-demand USD offer matches this GPU variant and count.NVIDIA DGX H100 (H100 SXM 80 GB) |
Output throughput is aggregate maximum-load performance, not single-user generation speed. Workloads show input → output tokens. Compare rows only when the model, GPU count, quantization, engine, and workload match.
Capacity scenarios combine an exact GPU variant/count rental with published output throughput from a different system. Throughput was not measured on the selected rental. Taxes, extras, idle time and minimum billing are excluded; these are not measured operating costs. Calculation guide.