Artus Dev Q5_K_S 27B Parameters

VRAM Requirements for
ToastyPigeon_gemma-3-27b-experiment-v2-merge-GGUF Q5_K_S

To run ToastyPigeon_gemma-3-27b-experiment-v2-merge-GGUF locally at Q5_K_S quantization, you need at minimum 21 GB of GPU VRAM.

21 GB
Required VRAM
18.56 GB
File Size
8K tokens
Context Window
27B
Parameters
Estimated VRAM Required
21
GB
High-End Consumer GPU
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $600 – $900

Used RTX 3090 (24GB)

24 GB

Best used-market value for 24GB VRAM. Solid for 30B-class models.

Balanced $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Fastest 24GB consumer GPU. Excellent for daily local inference.

Ultimate $2,500 – $3,500

NVIDIA A5000 (32GB)

32 GB

Pro workstation card with ECC memory. Maximum headroom at 24GB.

📊 VRAM Calculation Breakdown

Model File Size (Q5_K_S) 18.56 GB
Context Overhead (8,192 tokens × 27B × 2 ÷ 1M) 0.442 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 21 GB

Try a Different Quantization

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ToastyPigeon_gemma-3-27b-experiment-v2-merge-GGUF — Other Quantizations

Q5_K_S Current
21 GB
Q6_K 21.94 GB file
24.38 GB
Q8_0 28.69 GB file
31.13 GB
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Frequently Asked Questions

Can I run ToastyPigeon_gemma-3-27b-experiment-v2-merge-GGUF Q5_K_S on a consumer GPU?
Yes! At 21 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 ToastyPigeon_gemma-3-27b-experiment-v2-merge-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 Q5_K_S quality good enough for production?
Q5_K_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.