Artus Dev Q6_K 235B Parameters

VRAM Requirements for
Qwen3-235B-A22B-GGUF Q6_K

To run Qwen3-235B-A22B-GGUF locally at Q6_K quantization, you need at minimum 196.79 GB of GPU VRAM.

196.79 GB
Required VRAM
190.94 GB
File Size
8K tokens
Context Window
235B
Parameters
Estimated VRAM Required
196.79
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 36.8 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 36.8 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (Q6_K) 190.94 GB
Context Overhead (8,192 tokens × 235B × 2 ÷ 1M) 3.85 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 196.79 GB

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Qwen3-235B-A22B-GGUF — Other Quantizations

Q6_K Current
196.79 GB
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Frequently Asked Questions

Can I run Qwen3-235B-A22B-GGUF Q6_K on a consumer GPU?
Running Qwen3-235B-A22B-GGUF Q6_K locally requires 196.79 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 Qwen3-235B-A22B-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 Q6_K quality good enough for production?
Q6_K is suitable for specialized use cases. Check community benchmarks for specific quality metrics.