NVIDIA Tesla V100 16GB
The NVIDIA Tesla V100 16GB is an enterprise-class GPU built on the Volta architecture, featuring 16 GB of memory with 900 GB/s of memory bandwidth and a 250 W TDP. It delivers 14 TFLOPS of FP32 performance and 112 TFLOPS of FP16 tensor performance. Currently available from 6 providers starting at $0.03/GPU/hour, with a market median of $0.17/GPU/hour across 31 configurations.
Hardware specifications
Same across all providers
Median price across all providers
7 offerings from 6 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 10 | 31 GB | From$0.030 -55.6% 30d | From$0.03 | Launch | ||
| ×1 | 6 | 23 GB | -- | $0.170 | From$0.17 | Launch | |
Up to$1credit | ×8 | 128 | 2 TB | From$0.547 +0.2% 30d | From$4.38 | Launch | |
| ×8 | 92 | 448 GB | -- | $0.790 | $6.32 | ||
Theta EdgeCloudTrending | ×1 | 4 | 16 GB | -- | $0.990 | From$0.99 | Launch |
| ×1 | 8 | 30 GB | $2.570 0.0% 30d | $2.57 | Launch | ||
Sesterce CloudReferral link | ×1 | 8 | 30 GB | From$2.574 0.0% 30d | From$2.57 | Launch |
LLMs that fit on the NVIDIA Tesla V100 16GB
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 16 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB Too large | 155 GB Too large | 90 GB 8× · 38 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 8× · 50 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 8× · 42 GB free | 47 GB 4× · 17 GB free | 28 GB 2× · 4.4 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 8× · 49 GB free | 43 GB 4× · 21 GB free | 25 GB 2× · 6.8 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 8× · 53 GB free or 4× · 2% spare | 41 GB 4× · 23 GB free | 24 GB 2× · 8 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 8× · 53 GB free or 4× · 2% spare | 41 GB 4× · 23 GB free | 24 GB 2× · 8 GB free |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 8× · 61 GB free or 4× · 15% spare | 37 GB 4× · 27 GB free or 2× · 4% spare | 21 GB 2× · 11 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 4× · 6.4 GB free | 32 GB Barely fits on 2× | 18 GB 2× · 14 GB free or 1× · 4% spare |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 2× · 15 GB free or 1× · 16% spare |
| Gemma 3 12B Google | 12.2B | 29 GB 2× · 2.8 GB free | 16 GB 2× · 16 GB free or 1× · 19% spare | 9.4 GB 1× · 6.6 GB free |
| Granite 4.1 8B IBM | 8.8B | 21 GB 2× · 11 GB free | 12 GB 1× · 4.4 GB free | 6.8 GB 1× · 9.2 GB free |
| Qwen3 8B Alibaba | 8.2B | 20 GB 2× · 12 GB free | 11 GB 1× · 5.2 GB free | 6.3 GB 1× · 9.7 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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