The NVIDIA RTX 2070 Super is a consumer-grade GPU built on the Turing architecture, featuring 8 GB of memory with 448 GB/s of memory bandwidth and a 215 W TDP. It delivers 7.2 TFLOPS of FP32 performance. Currently available from 3 providers starting at $0.07/GPU/hour, with a market median of $0.11/GPU/hour across 4 configurations.
Hardware specifications
Same across all providers
Median price across all providers
3 offerings from 3 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×9 | 2 | 16 GB | From$0.070 +41.7% 30d | From$0.63 | Launch | ||
| ×1 | 16 | 63 GB | -- | $0.120 +33.3% 30d | $0.12 | Launch | |
Theta EdgeCloudTrending | ×1 | 8 | 8 GB | $0.120 -7.7% 30d | $0.12 | Launch |
LLMs that fit on the NVIDIA RTX 2070 Super
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 8 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB Tight on 9× · 10% spare |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB Tight on 9× · 0% spare | 47 GB 9× · 25 GB free | 28 GB 9× · 44 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB Tight on 9× · 9% spare | 43 GB 9× · 29 GB free | 25 GB 9× · 47 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB Tight on 9× · 15% spare | 41 GB 9× · 31 GB free | 24 GB 9× · 48 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB Tight on 9× · 15% spare | 41 GB 9× · 31 GB free | 24 GB 9× · 48 GB free |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 9× · 5.3 GB free | 37 GB 9× · 35 GB free | 21 GB 9× · 51 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 9× · 14 GB free | 32 GB 9× · 40 GB free | 18 GB 9× · 54 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 9× · 55 GB free |
| Gemma 3 12B Google | 12.2B | 29 GB 9× · 43 GB free | 16 GB 9× · 56 GB free | 9.4 GB 9× · 63 GB free or 1× · 2% spare |
| Granite 4.1 8B IBM | 8.8B | 21 GB 9× · 51 GB free | 12 GB 9× · 60 GB free | 6.8 GB 1× · 1.2 GB free |
| Qwen3 8B Alibaba | 8.2B | 20 GB 9× · 52 GB free | 11 GB 9× · 61 GB free | 6.3 GB 1× · 1.7 GB free |
| Llama 3.1 8B Instruct Meta | 8B | 19 GB 9× · 53 GB free | 11 GB 9× · 61 GB free | 6.2 GB 1× · 1.8 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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