Weifile Q5_K_M 32B Parameters

VRAM Requirements for
Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF Q5_K_M

To run Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF locally at Q5_K_M quantization, you need at minimum 24.52 GB of GPU VRAM.

24.52 GB
Required VRAM
22 GB
File Size
8K tokens
Context Window
32B
Parameters
Estimated VRAM Required
24.52
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q5_K_M) 22 GB
Context Overhead (8,192 tokens × 32B × 2 ÷ 1M) 0.524 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 24.52 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 10 GB file
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 Current
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 Q5_K_M on a consumer GPU?
Running Qwen2.5-Coder-32B-Instruct-NP-Abliterated-i1-GGUF Q5_K_M locally requires 24.52 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
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 Q5_K_M quality good enough for production?
Q5_K_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.