Momix 44 Q2_XXS 57B Parameters

VRAM Requirements for
Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated Q2_XXS

To run Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated locally at Q2_XXS quantization, you need at minimum 20.74 GB of GPU VRAM.

20.74 GB
Required VRAM
17.81 GB
File Size
8K tokens
Context Window
57B
Parameters
Estimated VRAM Required
20.74
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 (Q2_XXS) 17.81 GB
Context Overhead (8,192 tokens × 57B × 2 ÷ 1M) 0.934 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 20.74 GB

Try a Different Quantization

Use the interactive calculator to compare Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated across all available formats.

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Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated — Other Quantizations

Q2_K 17.81 GB file
20.74 GB
Q2_S 17.81 GB file
20.74 GB
Q2_XS 17.81 GB file
20.74 GB
Q2_XXS Current
20.74 GB
Q3_XXS 24.94 GB file
27.87 GB
Q3_K 24.94 GB file
27.87 GB
Q3_K_M 24.94 GB file
27.87 GB
Q3_K_S 24.94 GB file
27.87 GB
Q3_S 24.94 GB file
27.87 GB
Q3_XS 24.94 GB file
27.87 GB
Q4_XS 32.06 GB file
34.99 GB
Q4_K_S 32.06 GB file
34.99 GB
Q4_K_M 32.06 GB file
34.99 GB
Q5_K_M 39.19 GB file
42.12 GB
Q5_K_S 39.19 GB file
42.12 GB
Q6_K 46.31 GB file
49.24 GB
Q1_0 57 GB file
59.93 GB
Q1_S 57 GB file
59.93 GB
Q1_M 57 GB file
59.93 GB
Q8_0 60.56 GB file
63.49 GB
BF16 114 GB file
116.93 GB
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Frequently Asked Questions

Can I run Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated Q2_XXS on a consumer GPU?
Yes! At 20.74 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 Huihui-Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-abliterated?
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.