D J Lougen Q1_S 9B Parameters

VRAM Requirements for
Qwen3.5-9B-Harmonic-GGUF Q1_S

To run Qwen3.5-9B-Harmonic-GGUF locally at Q1_S quantization, you need at minimum 11.15 GB of GPU VRAM.

11.15 GB
Required VRAM
9 GB
File Size
8K tokens
Context Window
9B
Parameters
Estimated VRAM Required
11.15
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1.2 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 (Q1_S) 9 GB
Context Overhead (8,192 tokens × 9B × 2 ÷ 1M) 0.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 11.15 GB

Try a Different Quantization

Use the interactive calculator to compare Qwen3.5-9B-Harmonic-GGUF across all available formats.

Open Live Calculator →

Qwen3.5-9B-Harmonic-GGUF — Other Quantizations

Q2_M 2.81 GB file
4.96 GB
Q2_S 2.81 GB file
4.96 GB
Q2_XXS 2.81 GB file
4.96 GB
Q3_K_M 3.94 GB file
6.09 GB
Q3_K_S 3.94 GB file
6.09 GB
Q3_M 3.94 GB file
6.09 GB
Q3_S 3.94 GB file
6.09 GB
Q3_XXS 3.94 GB file
6.09 GB
Q4_K_M 5.06 GB file
7.21 GB
Q4_K_S 5.06 GB file
7.21 GB
Q4_NL 5.06 GB file
7.21 GB
Q4_XS 5.06 GB file
7.21 GB
Q5_K_M 6.19 GB file
8.34 GB
Q5_K_S 6.19 GB file
8.34 GB
Q6_K 7.31 GB file
9.46 GB
Q1_S Current
11.15 GB
Q1_M 9 GB file
11.15 GB
Q8_0 9.56 GB file
11.71 GB
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Frequently Asked Questions

Can I run Qwen3.5-9B-Harmonic-GGUF Q1_S on a consumer GPU?
Yes! At 11.15 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 Qwen3.5-9B-Harmonic-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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.