Weifile Q2_XS 32B Parameters

VRAM Requirements for
Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF Q2_XS

To run Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF locally at Q2_XS quantization, you need at minimum 12.52 GB of GPU VRAM.

12.52 GB
Required VRAM
10 GB
File Size
8K tokens
Context Window
32B
Parameters
Estimated VRAM Required
12.52
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $600 – $900

Used RTX 3090 (24GB)

24 GB

Best used-market value for 24GB VRAM. Solid for 30B-class models.

Balanced $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Fastest 24GB consumer GPU. Excellent for daily local inference.

Ultimate $2,500 – $3,500

NVIDIA A5000 (32GB)

32 GB

Pro workstation card with ECC memory. Maximum headroom at 24GB.

📊 VRAM Calculation Breakdown

Model File Size (Q2_XS) 10 GB
Context Overhead (8,192 tokens × 32B × 2 ÷ 1M) 0.524 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 12.52 GB

Try a Different Quantization

Use the interactive calculator to compare Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF across all available formats.

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Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF — Other Quantizations

Q2_K 10 GB file
12.52 GB
Q2_K_S 10 GB file
12.52 GB
Q2_M 10 GB file
12.52 GB
Q2_S 10 GB file
12.52 GB
Q2_XS Current
12.52 GB
Q2_XXS 10 GB file
12.52 GB
Q3_K_L 14 GB file
16.52 GB
Q3_K_M 14 GB file
16.52 GB
Q3_K_S 14 GB file
16.52 GB
Q3_M 14 GB file
16.52 GB
Q3_S 14 GB file
16.52 GB
Q3_XS 14 GB file
16.52 GB
Q3_XXS 14 GB file
16.52 GB
Q4_K_S 18 GB file
20.52 GB
Q4_K_M 18 GB file
20.52 GB
Q4_1 18 GB file
20.52 GB
Q4_0 18 GB file
20.52 GB
Q4_XS 18 GB file
20.52 GB
Q5_K_M 22 GB file
24.52 GB
Q5_K_S 22 GB file
24.52 GB
Q6_K 26 GB file
28.52 GB
Q1_S 32 GB file
34.52 GB
Q1_M 32 GB file
34.52 GB
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Frequently Asked Questions

Can I run Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF Q2_XS on a consumer GPU?
Yes! At 12.52 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q2_XS quality good enough for production?
Q2_XS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.