The NVIDIA RTX 4090 is a consumer-grade GPU built on the Ada Lovelace architecture, featuring 24 GB of memory with 1,008 GB/s of memory bandwidth and a 450 W TDP. It delivers 82.6 TFLOPS of FP32 performance. Currently available from 6 providers starting at $0.12/GPU/hour, with a market median of $0.51/GPU/hour across 62 configurations.
Hardware specifications
Same across all providers
Median price across all providers
6 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 | 12 | 31 GB | $0.740 | $0.74 | Launch | ||
| ×1 | 18 | 126 GB | From$0.120 +22.5% 30d | From$0.12 | Launch | ||
| ×1 | 12 | 31 GB | From$0.370 -16.7% 30d | From$0.37 | Launch | ||
| ×1 | 12 | 70 GB | $0.440 0.0% 30d | From$0.44 | Launch | ||
Sesterce CloudReferral link | ×8 | 88 | 228 GB | From$0.440 +50.0% 30d | From$3.52 | Launch | |
Novita AIReferral link | ×1 | 16 | 125 GB | $0.670 0.0% 30d | From$0.67 | Launch | |
Up to$500credit | ×1 | 12 | 31 GB | $0.740 0.0% 30d | $0.74 | Launch |
LLMs that fit on the NVIDIA RTX 4090
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 24 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 Barely fits on 8× |
| Solar Open2 250B Upstage | 250B 15B active | — Not released | 330 GB 14× · 5.6 GB free | 192 GB 14× · 144 GB free or 8× · 19% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | — Not released | 310 GB 14× · 26 GB free | 181 GB 8× · 11 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 14× · 54 GB free | 155 GB 8× · 37 GB free or 6× · 11% spare | 90 GB 4× · 5.7 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 4× · 18 GB free or 3× · 10% spare |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 4× · 9.7 GB free or 3× · 0% spare | 47 GB Barely fits on 2× | 28 GB 2× · 20 GB free or 1× · 4% spare |
| Qwen3 32B Alibaba | 32.8B | 79 GB 4× · 17 GB free or 3× · 9% spare | 43 GB 2× · 4.8 GB free | 25 GB 2× · 23 GB free or 1× · 14% spare |
| Gemma 4 31B Google | 31.3B | 75 GB 4× · 21 GB free or 3× · 15% spare | 41 GB 2× · 6.7 GB free | 24 GB 2× · 24 GB free or 1× · 19% spare |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 4× · 21 GB free or 3× · 15% spare | 41 GB 2× · 6.8 GB free | 24 GB Barely fits on 1× |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 3× · 5.3 GB free | 37 GB 2× · 11 GB free | 21 GB 1× · 2.7 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 3× · 14 GB free | 32 GB 2× · 16 GB free | 18 GB 1× · 5.6 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 1× · 7.5 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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