Run SulphurAI/Sulphur-2-base locally
SulphurAI/Sulphur-2-base is a large language model with 9.2 billion parameters, built on the qwen35 architecture. It has been downloaded 799,631 times.
To run SulphurAI/Sulphur-2-base locally at a 4,096-token context, its quantized versions need between 2.13 GB (GGUF, lowest quality) and 19.26 GB (Q8_0, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is GGUF, needing about 2.13 GB. That means SulphurAI/Sulphur-2-base fits entirely in the VRAM of a 6 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| GGUF | 0.8 | Very low | 0.86 GB | 0.47 GB | 2.13 GB | 466.0 t/s | Fits in VRAM |
| BF16 | 0.8 | Very low | 0.86 GB | 0.47 GB | 2.13 GB | 466.0 t/s | Fits in VRAM |
| Q8_0 | 16.8 | Excellent | 17.99 GB | 0.47 GB | 19.26 GB | 2.8 t/s | Offload |
KV cache computed from the model's exact architecture. Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run SulphurAI/Sulphur-2-base?
You need about 2.13 GB of VRAM to run SulphurAI/Sulphur-2-base entirely on the GPU using the GGUF quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run SulphurAI/Sulphur-2-base on an 8 GB GPU?
Yes. With 8 GB of VRAM you can run SulphurAI/Sulphur-2-base fully on the GPU using GGUF (about 2.13 GB).
Can I run SulphurAI/Sulphur-2-base on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run SulphurAI/Sulphur-2-base fully on the GPU using GGUF (about 2.13 GB).
Can I run SulphurAI/Sulphur-2-base on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run SulphurAI/Sulphur-2-base fully on the GPU using Q8_0 (about 19.26 GB).
What is the best quantization for SulphurAI/Sulphur-2-base?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.