Google DeepMind Q6_K 27B Parameters

VRAM Requirements for
Gemma 2 27B Q6_K

To run Gemma 2 27B locally at Q6_K quantization, you need at minimum 22.8 GB of GPU VRAM.

22.8 GB
Required VRAM
20.31 GB
File Size
8K tokens
Context Window
27B
Parameters
Estimated VRAM Required
22.8
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 (Q6_K) 20.31 GB
Context Overhead (8,192 tokens × 27B × 2 ÷ 1M) 0.442 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 22.8 GB

Try a Different Quantization

Use the interactive calculator to compare Gemma 2 27B across all available formats.

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Gemma 2 27B — Other Quantizations

Q4_K_M 15.64 GB file
18.1 GB
Q6_K Current
22.8 GB
Q8_0 28.97 GB file
31.4 GB
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Frequently Asked Questions

Can I run Gemma 2 27B Q6_K on a consumer GPU?
Yes! At 22.8 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 Gemma 2 27B?
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 Q6_K quality good enough for production?
Q6_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.