The NVIDIA RTX A6000 is an enterprise-class GPU built on the Ampere architecture, featuring 48 GB of memory with 768 GB/s of memory bandwidth and a 300 W TDP. It delivers 38.7 TFLOPS of FP32 performance. Currently available from 9 providers starting at $0.28/GPU/hour, with a market median of $0.55/GPU/hour across 28 configurations.
Hardware specifications
Same across all providers
Median price across all providers
10 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 | 9 | 50 GB | $0.530 | $0.53 | Launch | ||
| ×1 | 16 | 126 GB | From$0.280 +34.3% 30d | From$0.28 | Launch | ||
Thunder ComputeReferral link | ×1 | 4 | 32 GB | -- | $0.350 | $0.35 | Launch |
| ×1 | 28 | 58 GB | $0.500 0.0% 30d | From$0.50 | Launch | ||
Up to$500credit | ×1 | 9 | 50 GB | $0.530 | $0.53 | Launch | |
| ×1 | 8 | 32 GB | $0.550 0.0% 30d | $0.55 | Launch | ||
| ×1 | 28 | 58 GB | $0.550 -12.7% 30d | From$0.55 | Launch | ||
| ×1 | 6 | 48 GB | From$0.600 0.0% 30d | From$0.60 | Launch | ||
| ×1 | 10 | 60 GB | -- | $0.610 | From$0.61 | Launch | |
Sesterce CloudReferral link | ×1 | 12 | 64 GB | From$0.627 0.0% 30d | From$0.63 | Launch | |
| ×1 | 14 | 100 GB | -- | $1.090 | From$1.09 |
LLMs that fit on the NVIDIA RTX A6000
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 48 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 4× |
| Solar Open2 250B Upstage | 250B 15B active | 601 GB Too large | 330 GB 8× · 54 GB free or 6× · 4% spare | 192 GB 6× · 96 GB free or 4× · 19% spare |
| Qwen3 235B-A22B Alibaba | 235B 22B active | 564 GB Too large | 310 GB 8× · 74 GB free or 6× · 11% spare | 181 GB 4× · 11 GB free |
| Laguna-S 2.1 Poolside | 118B 8B active | 282 GB 6× · 5.8 GB free | 155 GB 4× · 37 GB free | 90 GB 2× · 5.7 GB free |
| gpt-oss-120b OpenAI | 117B 5.1B active | — Not released | — Not released | 78 GB 2× · 18 GB free |
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB 2× · 9.7 GB free | 47 GB Barely fits on 1× | 28 GB 1× · 20 GB free |
| Qwen3 32B Alibaba | 32.8B | 79 GB 2× · 17 GB free | 43 GB 1× · 4.8 GB free | 25 GB 1× · 23 GB free |
| Gemma 4 31B Google | 31.3B | 75 GB 2× · 21 GB free | 41 GB 1× · 6.7 GB free | 24 GB 1× · 24 GB free |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB 2× · 21 GB free | 41 GB 1× · 6.8 GB free | 24 GB 1× · 24 GB free |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB 2× · 29 GB free | 37 GB 1× · 11 GB free | 21 GB 1× · 27 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB 2× · 38 GB free | 32 GB 1× · 16 GB free | 18 GB 1× · 30 GB free |
| gpt-oss-20b OpenAI | 21B 3.6B active | — Not released | — Not released | 17 GB 1× · 31 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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