The NVIDIA RTX 5090 is a consumer-grade GPU built on the Blackwell architecture, featuring 32 GB of memory with 1,792 GB/s of memory bandwidth and a 575 W TDP. It delivers 104 TFLOPS of FP32 performance. Currently available from 9 providers starting at $0.33/GPU/hour, with a market median of $0.69/GPU/hour across 63 configurations.
Hardware specifications
Same across all providers
Median price across all providers
9 offerings from 9 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 92 GB | $0.990 | $0.99 | Launch | ||
| ×1 | 32 | 63 GB | From$0.330 +1.0% 30d | From$0.33 | Launch | ||
Nova CloudReferral link | ×1 | 22 | 84 GB | $0.570 +5.6% 30d | $0.57 | Launch | |
| ×1 | — | 73 GB | $0.625 0.0% 30d | $0.63 | Launch | ||
| ×4 | 60 | 480 GB | From$0.683 +11.4% 30d | From$2.73 | Launch | ||
Sesterce CloudReferral link | ×1 | 12 | 120 GB | From$0.715 -3.7% 30d | From$0.72 | Launch | |
| ×1 | 12 | 120 GB | $0.720 0.0% 30d | From$0.72 | Launch | ||
Theta EdgeCloudTrending | ×1 | 32 | 126 GB | $0.720 +12.5% 30d | $0.72 | Launch | |
Novita AIReferral link | ×1 | 16 | 96 GB | From$0.730 0.0% 30d | From$0.73 | Launch | |
Up to$500credit | ×1 | 16 | 92 GB | $0.990 | $0.99 | Launch |
LLMs that fit on the NVIDIA RTX 5090
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 32 GB per GPU. Showing the 12 largest that fit.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| DeepSeek V4 Flash DeepSeek | 284B | — Not released | — Not released | 192 GB 8× · 64 GB free or 5× · 0% spare |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB Too large | 330 GB Too large | 192 GB 8× · 64 GB free |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB Too large | 310 GB Too large | 181 GB 8× · 75 GB free or 5× · 6% spare |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB Tight on 8× · 8% spare | 155 GB 5× · 4.8 GB free | 90 GB 3× · 5.7 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 3× · 18 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 3× · 9.7 GB free | 47 GB 2× · 17 GB free | 28 GB 1× · 4.4 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 3× · 17 GB free | 43 GB 2× · 21 GB free | 25 GB 1× · 6.8 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 3× · 21 GB free or 2× · 2% spare | 41 GB 2× · 23 GB free | 24 GB 1× · 8 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 3× · 21 GB free or 2× · 2% spare | 41 GB 2× · 23 GB free | 24 GB 1× · 8 GB free |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 3× · 29 GB free or 2× · 15% spare | 37 GB 2× · 27 GB free or 1× · 4% spare | 21 GB 1× · 11 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 2× · 6.4 GB free | 32 GB Barely fits on 1× | 18 GB 1× · 14 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 1× · 15 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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