NVIDIA A100 80GB
The NVIDIA A100 80GB is an enterprise-class GPU built on the Ampere architecture, featuring 80 GB of memory with 2,000 GB/s of memory bandwidth and a 400 W TDP. It delivers 19.5 TFLOPS of FP32 performance and 312 TFLOPS of FP16 tensor performance. Currently available from 15 providers starting at $0.27/GPU/hour, with a market median of $1.49/GPU/hour across 83 configurations.

A100 80GB · PCIe reference design
PCIe illustration. Listed configurations may use SXM or other variants.
Hardware specifications
Same across all providers
Median price across all providers
15 offerings from 15 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 117 GB | $1.790 | $1.79 | Launch | ||
| ×1 | 32 | 188 GB | From$0.270 -15.6% 30d | From$0.27 | Launch | ||
Up to$100credit | ×1 | — | — | From$1.070 | From$1.07 | Launch | |
Thunder ComputeReferral link | ×1 | 8 | 64 GB | -- | From$1.090 0.0% 30d | From$1.09 | Launch |
| ×8 | 252 | 1.88 TB | From$1.400 +3.7% 30d | From$11.20 | Launch | ||
| ×8 | 240 | 1.25 TB | From$1.418 +25.0% 30d | From$11.34 | Launch | ||
Sesterce CloudReferral link | ×8 | 240 | 1.25 TB | From$1.485 +1.0% 30d | From$11.88 | Launch | |
| ×1 | 16 | 120 GB | $1.490 0.0% 30d | From$1.49 | Launch | ||
OblivusReferral link | ×8 | 252 | 1.88 TB | From$1.570 0.0% 30d | From$12.56 | Launch | |
| ×1 | 16 | 120 GB | From$1.700 -9.5% 30d | From$1.70 | Launch | ||
| ×1 | 16 | 160 GB | $1.760 +18.1% 30d | From$1.76 | Launch | ||
Up to$500credit | ×1 | 16 | 117 GB | $1.790 0.0% 30d | $1.79 | Launch | |
| ×1 | 22 | 120 GB | -- | From$1.927 +11.0% 30d | From$1.93 | Launch | |
Up to$1credit | ×8 | 64 | 512 GB | From$1.985 -1.1% 30d | From$15.88 | Launch | |
| ×1 | 30 | 200 GB | From$1.990 -16.7% 30d | From$1.99 | Launch | ||
Theta EdgeCloudTrending | ×1 | 10 | 80 GB | -- | $1.990 | From$1.99 | Launch |
LLMs that fit on the NVIDIA A100 80GB
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 80 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| Kimi K2.5 Moonshot AI | 1T 32B active | — Not released | — Not released | 714 GB Tight on 8× · 7% spare |
| GLM-5.2 Z.ai | 753B | — Not released | 994 GB Too large | 579 GB 8× · 61 GB free |
| DeepSeek R1 DeepSeek | 671B 37B active | — Not released | 826 GB Too large | 481 GB 8× · 159 GB free |
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 4× · 128 GB free or 2× · 0% spare |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB 8× · 39 GB free | 330 GB 8× · 310 GB free or 4× · 16% spare | 192 GB 4× · 128 GB free |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB 8× · 76 GB free | 310 GB 4× · 9.7 GB free | 181 GB 4× · 139 GB free or 2× · 6% spare |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 4× · 38 GB free | 155 GB 2× · 4.8 GB free | 90 GB 2× · 70 GB free or 1× · 6% spare |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 1× · 1.7 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 2× · 74 GB free or 1× · 11% spare | 47 GB 1× · 33 GB free | 28 GB 1× · 52 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 1× · 1.4 GB free | 43 GB 1× · 37 GB free | 25 GB 1× · 55 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 1× · 4.9 GB free | 41 GB 1× · 39 GB free | 24 GB 1× · 56 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 1× · 5.1 GB free | 41 GB 1× · 39 GB free | 24 GB 1× · 56 GB free |
Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this · Start from a model instead
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