The NVIDIA A30 is an enterprise-class GPU built on the Ampere architecture, featuring 24 GB of memory with 933 GB/s of memory bandwidth and a 165 W TDP. It delivers 10.3 TFLOPS of FP32 performance and 165 TFLOPS of FP16 tensor performance. Currently available from 1 provider starting at $0.42/GPU/hour, with a market median of $0.42/GPU/hour across 1 configurations.
Hardware specifications
Same across all providers
Median price across all providers
3 offerings from 1 providers
Sorted by price ascending. Compare configurations side-by-side.
| Provider | Count | vCPU | RAM | Region | Per GPU hour | Total/hr | Action |
|---|---|---|---|---|---|---|---|
 Vast.aiPromoted | ×1 | 16 | 31 GB |  | $0.420 | $0.42 | Launch |
 Sesterce CloudReferral link | ×1 | 16 | 48 GB | -- | $0.385 | From$0.39 | |
| ×1 | 16 | 31 GB | | $0.420 +44.8% 30d | $0.42 | Launch | |
| ×1 | — | — | -- | $0.550 | $0.55 |
LLMs that fit on the NVIDIA A30
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 |
|---|---|---|---|---|
| Qwen3.6 35B-A3B Alibaba | 35B 3B active | 86 GB Too large | 47 GB Too large | 28 GB Tight on 1× · 4% spare |
| Qwen3 32B Alibaba | 32.8B | 79 GB Too large | 43 GB Too large | 25 GB Tight on 1× · 14% spare |
| Gemma 4 31B Google | 31.3B | 75 GB Too large | 41 GB Too large | 24 GB Tight on 1× · 19% spare |
| GLM-4.7 Flash Z.ai | 31.2B | 75 GB Too large | 41 GB Too large | 24 GB Barely fits on 1× |
| Qwen3.6 27B Alibaba | 27.8B | 67 GB Too large | 37 GB Too large | 21 GB 1× · 2.7 GB free |
| Mistral Small 3.2 24B Mistral AI | 24B | 58 GB Too large | 32 GB Too large | 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 |
| Gemma 3 12B Google | 12.2B | 29 GB Too large | 16 GB 1× · 7.9 GB free | 9.4 GB 1× · 15 GB free |
| Granite 4.1 8B IBM | 8.8B | 21 GB 1× · 2.9 GB free | 12 GB 1× · 12 GB free | 6.8 GB 1× · 17 GB free |
| Qwen3 8B Alibaba | 8.2B | 20 GB 1× · 4.3 GB free | 11 GB 1× · 13 GB free | 6.3 GB 1× · 18 GB free |
| Llama 3.1 8B Instruct Meta | 8B | 19 GB 1× · 4.7 GB free | 11 GB 1× · 13 GB free | 6.2 GB 1× · 18 GB free |
| Mistral 7B Instruct v0.3 Mistral AI | 7.2B | 17 GB 1× · 6.6 GB free | 9.6 GB 1× · 14 GB free | 5.6 GB 1× · 18 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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