Bartowski Q2_M 24B Parameters

VRAM Requirements for
BeaverAI_MS-2501-DPE-QwQify-v0.1-24B-GGUF Q2_M

To run BeaverAI_MS-2501-DPE-QwQify-v0.1-24B-GGUF locally at Q2_M quantization, you need at minimum 9.89 GB of GPU VRAM.

9.89 GB
Required VRAM
7.5 GB
File Size
8K tokens
Context Window
24B
Parameters
Estimated VRAM Required
9.89
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

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_M) 7.5 GB
Context Overhead (8,192 tokens × 24B × 2 ÷ 1M) 0.393 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 9.89 GB

Try a Different Quantization

Use the interactive calculator to compare BeaverAI_MS-2501-DPE-QwQify-v0.1-24B-GGUF across all available formats.

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BeaverAI_MS-2501-DPE-QwQify-v0.1-24B-GGUF — Other Quantizations

Q2_K 7.5 GB file
9.89 GB
Q2_K_L 7.5 GB file
9.89 GB
Q2_M Current
9.89 GB
Q2_S 7.5 GB file
9.89 GB
Q2_XS 7.5 GB file
9.89 GB
Q3_K_L 10.5 GB file
12.89 GB
Q3_K_M 10.5 GB file
12.89 GB
Q3_K_S 10.5 GB file
12.89 GB
Q3_K_XL 10.5 GB file
12.89 GB
Q3_M 10.5 GB file
12.89 GB
Q3_XS 10.5 GB file
12.89 GB
Q3_XXS 10.5 GB file
12.89 GB
Q4_0 13.5 GB file
15.89 GB
Q4_XS 13.5 GB file
15.89 GB
Q4_NL 13.5 GB file
15.89 GB
Q4_K_S 13.5 GB file
15.89 GB
Q4_K_M 13.5 GB file
15.89 GB
Q4_K_L 13.5 GB file
15.89 GB
Q4_1 13.5 GB file
15.89 GB
Q5_K_L 16.5 GB file
18.89 GB
Q5_K_M 16.5 GB file
18.89 GB
Q5_K_S 16.5 GB file
18.89 GB
Q6_K 19.5 GB file
21.89 GB
Q6_K_L 19.5 GB file
21.89 GB
Q8_0 25.5 GB file
27.89 GB
BF16 48 GB file
50.39 GB
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Frequently Asked Questions

Can I run BeaverAI_MS-2501-DPE-QwQify-v0.1-24B-GGUF Q2_M on a consumer GPU?
Yes! At 9.89 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 BeaverAI_MS-2501-DPE-QwQify-v0.1-24B-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_M quality good enough for production?
Q2_M is suitable for specialized use cases. Check community benchmarks for specific quality metrics.