The NVIDIA RTX 3080 is a consumer-grade GPU built on the Ampere architecture, featuring 10 GB of memory with 760 GB/s of memory bandwidth and a 320 W TDP. It delivers 29.8 TFLOPS of FP32 performance. Currently available from 2 providers starting at $0.03/GPU/hour, with a market median of $0.14/GPU/hour across 20 configurations.
Hardware specifications
Same across all providers
Median price across all providers
2 offerings from 2 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 63 GB | From$0.030 -8.7% 30d | From$0.03 | Launch | ||
Theta EdgeCloudTrending | ×1 | 12 | 58 GB | From$0.130 -2.4% 30d | From$0.13 | Launch |
LLMs that fit on the NVIDIA RTX 3080
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 10 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 | — Not released | 155 GB Too large | 90 GB Tight on 8× · 6% spare |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 8× · 1.7 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB Tight on 8× · 11% spare | 47 GB 6× · 13 GB free | 28 GB 6× · 32 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 8× · 1.4 GB free | 43 GB 6× · 17 GB free | 25 GB 6× · 35 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 8× · 4.9 GB free | 41 GB 6× · 19 GB free | 24 GB 6× · 36 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 8× · 5.1 GB free | 41 GB 6× · 19 GB free | 24 GB 6× · 36 GB free or 2× · 0% spare |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 8× · 13 GB free or 6× · 7% spare | 37 GB 6× · 23 GB free | 21 GB 6× · 39 GB free or 2× · 12% spare |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 6× · 2.4 GB free | 32 GB 6× · 28 GB free | 18 GB 2× · 1.6 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 2× · 3.5 GB free |
| Gemma 3 12B Google | 12.2B | 29 GB 6× · 31 GB free | 16 GB 2× · 3.9 GB free | 9.4 GB Barely fits on 1× |
| Granite 4.1 8B IBM | 8.8B | 21 GB 6× · 39 GB free or 2× · 13% spare | 12 GB 2× · 8.4 GB free or 1× · 3% spare | 6.8 GB 1× · 3.2 GB free |
| Qwen3 8B Alibaba | 8.2B | 20 GB Barely fits on 2× | 11 GB 2× · 9.2 GB free or 1× · 10% spare | 6.3 GB 1× · 3.7 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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