Xing1217 Q8_0 31B Parameters

VRAM Requirements for
Gemma-4-31B-JANG_4M-CRACK-GGUF Q8_0

To run Gemma-4-31B-JANG_4M-CRACK-GGUF locally at Q8_0 quantization, you need at minimum 35.45 GB of GPU VRAM.

35.45 GB
Required VRAM
32.94 GB
File Size
8K tokens
Context Window
31B
Parameters
Estimated VRAM Required
35.45
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 32.94 GB
Context Overhead (8,192 tokens × 31B × 2 ÷ 1M) 0.508 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 35.45 GB

Try a Different Quantization

Use the interactive calculator to compare Gemma-4-31B-JANG_4M-CRACK-GGUF across all available formats.

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Gemma-4-31B-JANG_4M-CRACK-GGUF — Other Quantizations

Q3_K_M 13.56 GB file
16.07 GB
Q4_K_M 17.44 GB file
19.95 GB
Q8_0 Current
35.45 GB
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Frequently Asked Questions

Can I run Gemma-4-31B-JANG_4M-CRACK-GGUF Q8_0 on a consumer GPU?
Running Gemma-4-31B-JANG_4M-CRACK-GGUF Q8_0 locally requires 35.45 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
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-4-31B-JANG_4M-CRACK-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 Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.