David A U Q2_S 30B Parameters

VRAM Requirements for
Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf Q2_S

To run Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf locally at Q2_S quantization, you need at minimum 11.87 GB of GPU VRAM.

11.87 GB
Required VRAM
9.38 GB
File Size
8K tokens
Context Window
30B
Parameters
Estimated VRAM Required
11.87
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 1.9 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 (Q2_S) 9.38 GB
Context Overhead (8,192 tokens × 30B × 2 ÷ 1M) 0.492 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 11.87 GB

Try a Different Quantization

Use the interactive calculator to compare Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf across all available formats.

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Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf — Other Quantizations

Q2_M 9.38 GB file
11.87 GB
Q2_S Current
11.87 GB
Q3_M 13.13 GB file
15.62 GB
Q3_S 13.13 GB file
15.62 GB
Q3_XS 13.13 GB file
15.62 GB
Q4_K_M 16.88 GB file
19.37 GB
Q4_K_S 16.88 GB file
19.37 GB
Q4_XS 16.88 GB file
19.37 GB
Q5_K_M 20.63 GB file
23.12 GB
Q6_K 24.38 GB file
26.87 GB
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Frequently Asked Questions

Can I run Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-gguf Q2_S on a consumer GPU?
Yes! At 11.87 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 Qwen3-30B-A1.5B-64K-High-Speed-NEO-Imatrix-MAX-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_S quality good enough for production?
Q2_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.