Marsupial A I Q2_0 27B Parameters

VRAM Requirements for
Big-Tiger-Gemma-27B-v1_iMatrix_GGUF Q2_0

To run Big-Tiger-Gemma-27B-v1_iMatrix_GGUF locally at Q2_0 quantization, you need at minimum 10.88 GB of GPU VRAM.

10.88 GB
Required VRAM
8.44 GB
File Size
8K tokens
Context Window
27B
Parameters
Estimated VRAM Required
10.88
GB
Mid-Range GPU Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 0.9 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_0) 8.44 GB
Context Overhead (8,192 tokens × 27B × 2 ÷ 1M) 0.442 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 10.88 GB

Try a Different Quantization

Use the interactive calculator to compare Big-Tiger-Gemma-27B-v1_iMatrix_GGUF across all available formats.

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Big-Tiger-Gemma-27B-v1_iMatrix_GGUF — Other Quantizations

Q2_0 Current
10.88 GB
Q3_0 11.81 GB file
14.25 GB
Q4_0 15.19 GB file
17.63 GB
Q5_0 18.56 GB file
21 GB
Q6_0 21.94 GB file
24.38 GB
FP16 54 GB file
56.44 GB
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Frequently Asked Questions

Can I run Big-Tiger-Gemma-27B-v1_iMatrix_GGUF Q2_0 on a consumer GPU?
Yes! At 10.88 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 Big-Tiger-Gemma-27B-v1_iMatrix_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_0 quality good enough for production?
Q2_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.