Bobchenyx Q2_XXS 192B Parameters

VRAM Requirements for
DeepSeek-V3-0324-MoE-Pruner-E192-GGUF Q2_XXS

To run DeepSeek-V3-0324-MoE-Pruner-E192-GGUF locally at Q2_XXS quantization, you need at minimum 65.15 GB of GPU VRAM.

65.15 GB
Required VRAM
60 GB
File Size
8K tokens
Context Window
192B
Parameters
Estimated VRAM Required
65.15
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 17.2 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (Q2_XXS) 60 GB
Context Overhead (8,192 tokens × 192B × 2 ÷ 1M) 3.146 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 65.15 GB

Try a Different Quantization

Use the interactive calculator to compare DeepSeek-V3-0324-MoE-Pruner-E192-GGUF across all available formats.

Open Live Calculator →

DeepSeek-V3-0324-MoE-Pruner-E192-GGUF — Other Quantizations

Q2_XXS Current
65.15 GB
Q1_M 192 GB file
197.15 GB
Q1_S 192 GB file
197.15 GB
Advertisement Zone

Frequently Asked Questions

Can I run DeepSeek-V3-0324-MoE-Pruner-E192-GGUF Q2_XXS on a consumer GPU?
Running DeepSeek-V3-0324-MoE-Pruner-E192-GGUF Q2_XXS locally requires 65.15 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 DeepSeek-V3-0324-MoE-Pruner-E192-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_XXS quality good enough for production?
Q2_XXS is suitable for specialized use cases. Check community benchmarks for specific quality metrics.