Dev Quasar Q4_K_S 16B Parameters

VRAM Requirements for
huihui-ai.Qwen3-16B-A3B-abliterated-GGUF Q4_K_S

To run huihui-ai.Qwen3-16B-A3B-abliterated-GGUF locally at Q4_K_S quantization, you need at minimum 11.26 GB of GPU VRAM.

11.26 GB
Required VRAM
9 GB
File Size
8K tokens
Context Window
16B
Parameters
Estimated VRAM Required
11.26
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1.3 GB short
Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (Q4_K_S) 9 GB
Context Overhead (8,192 tokens × 16B × 2 ÷ 1M) 0.262 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 11.26 GB

Try a Different Quantization

Use the interactive calculator to compare huihui-ai.Qwen3-16B-A3B-abliterated-GGUF across all available formats.

Open Live Calculator →

huihui-ai.Qwen3-16B-A3B-abliterated-GGUF — Other Quantizations

Q2_K 5 GB file
7.26 GB
Q3_K_L 7 GB file
9.26 GB
Q3_K_M 7 GB file
9.26 GB
Q3_K_S 7 GB file
9.26 GB
Q4_K_M 9 GB file
11.26 GB
Q4_K_S Current
11.26 GB
Q5_K_M 11 GB file
13.26 GB
Q5_K_S 11 GB file
13.26 GB
Q6_K 13 GB file
15.26 GB
Q8_0 17 GB file
19.26 GB
Advertisement Zone

Frequently Asked Questions

Can I run huihui-ai.Qwen3-16B-A3B-abliterated-GGUF Q4_K_S on a consumer GPU?
Yes! At 11.26 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 huihui-ai.Qwen3-16B-A3B-abliterated-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 Q4_K_S quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.