The A2-8G features 8 GB of memory.
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LLMs that fit on the A2-8G
Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 8 GB per GPU.
| Model | Parameters | 16-bit | 8-bit | 4-bit |
|---|---|---|---|---|
| Gemma 3 12B Google | 12.2B | 29 GB Too large | 16 GB Too large | 9.4 GB Tight on 1× · 2% spare |
| Granite 4.1 8B IBM | 8.8B | 21 GB Too large | 12 GB Too large | 6.8 GB 1× · 1.2 GB free |
| Qwen3 8B Alibaba | 8.2B | 20 GB Too large | 11 GB Too large | 6.3 GB 1× · 1.7 GB free |
| Llama 3.1 8B Instruct Meta | 8B | 19 GB Too large | 11 GB Too large | 6.2 GB 1× · 1.8 GB free |
| Mistral 7B Instruct v0.3 Mistral AI | 7.2B | 17 GB Too large | 9.6 GB Tight on 1× · 0% spare | 5.6 GB 1× · 2.4 GB free |
| Qwen3 4B Alibaba | 4B | 9.7 GB Too large | 5.3 GB 1× · 2.7 GB free | 3.1 GB 1× · 4.9 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
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Frequently asked questions
How much VRAM does the A2-8G have?
The A2-8G comes with 8 GB of VRAM, which determines the largest models and batch sizes it can hold in memory for training and inference.
Which LLMs can I run on the A2-8G?
With 8 GB of VRAM, a single A2-8G can serve models such as Granite 4.1 8B, Qwen3 8B, Llama 3.1 8B Instruct, Mistral 7B Instruct v0.3. The table above lists the estimated VRAM for each model at 16-bit, 8-bit and 4-bit precision, including the multi-GPU configurations needed for larger models.