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 11 providers starting at $0.26/GPU/hour, with a market median of $0.67/GPU/hour across 69 configurations.

RTX 5090 · Founders Edition reference
Representative design. Provider hardware may differ.
Hardware specifications
Same across all providers
Median price across all providers
13 offerings from 11 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
| ×1 | 16 | 93 GB | $1.190 | $1.19 | Launch | ||
| ×1 | 24 | 31 GB | From$0.260 +31.3% 30d | From$0.26 | Launch | ||
| ×1 | 24 | 63 GB | From$0.400 -19.9% 30d | From$0.40 | Launch | ||
| ×1 | 8 | 30 GB | -- | From$0.500 +4.4% 30d | From$0.50 | Launch | |
LiumReferral link | ×8 | 384 | 251 GB | From$0.500 +14.0% 30d | From$4.00 | Launch | |
Novita AIReferral link | ×1 | 16 | 58 GB | From$0.560 | From$0.56 | ||
Up to$100credit | ×1 | — | — | $0.570 0.0% 30d | $0.57 | ||
| ×1 | — | 73 GB | $0.580 | $0.58 | Launch | ||
Sesterce CloudReferral link | ×1 | 12 | 120 GB | From$0.715 -7.1% 30d | From$0.72 | Launch | |
Nova CloudReferral link | ×1 | 22 | 84 GB | $0.720 +14.3% 30d | From$0.72 | Launch | |
| ×1 | 12 | 120 GB | $0.850 +10.4% 30d | $0.85 | Launch | ||
Up to$500credit | ×1 | 16 | 93 GB | $1.190 0.0% 30d | $1.19 | Launch | |
Up to$200credit | ×1 | Billed separately | Billed separately | -- | $1.290 0.0% 30d | From$1.29 | Launch |
| ×2 | 32 | 63 GB | From$1.300 +35.0% 30d | From$2.60 | 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 |
| 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 |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB Tight on 8× · 8% spare | 155 GB 8× · 101 GB free | 90 GB 4× · 38 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 4× · 50 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 4× · 42 GB free | 47 GB 2× · 17 GB free | 28 GB 1× · 4.4 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 4× · 49 GB free | 43 GB 2× · 21 GB free | 25 GB 1× · 6.8 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 4× · 53 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 4× · 53 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 4× · 61 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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