David A U Q3_0 8B Parameters

VRAM Requirements for
L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf Q3_0

To run L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf locally at Q3_0 quantization, you need at minimum 5.63 GB of GPU VRAM.

5.63 GB

Required VRAM

3.5 GB

File Size

8K tokens

Context Window

Parameters

Estimated VRAM Required

5.63

Consumer Friendly

0 8GB
RTX 3060 16GB
RTX 3080 24GB
RTX 4090 48GB
A6000 80GB
A100 80GB+

Recommended GPU Configurations

Budget $299 – $349

RTX 4060 (8GB)

8 GB

Perfect entry-level GPU. Handles small quantised models with ease.

Balanced $549 – $599

RTX 4070 (12GB)

12 GB

Excellent performance-per-dollar for running sub-7B models at Q8.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Overkill for this size — plenty of headroom for bigger models.

📊 VRAM Calculation Breakdown

Model File Size (Q3_0) 3.5 GB

Context Overhead (8,192 tokens × 8B × 2 ÷ 1M) 0.131 GB

System Buffer (OS + CUDA runtime) 2.00 GB

Total Required VRAM 5.63 GB

Try a Different Quantization

Use the interactive calculator to compare L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf across all available formats.

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L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf — Other Quantizations

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Frequently Asked Questions

Can I run L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf Q3_0 on a consumer GPU?

Yes! At 5.63 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 L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-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 Q3_0 quality good enough for production?

Q3_0 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.

VRAM Requirements for L3.1-MOE-2X8B-Deepseek-DeepHermes-e32-uncensored-abliterated-13.7B-gguf Q3_0