Unsloth Q6_K_XL 80B Parameters

VRAM Requirements for
Qwen3-Next-80B-A3B-Instruct-GGUF Q6_K_XL

To run Qwen3-Next-80B-A3B-Instruct-GGUF locally at Q6_K_XL quantization, you need at minimum 68.31 GB of GPU VRAM.

68.31 GB
Required VRAM
65 GB
File Size
8K tokens
Context Window
80B
Parameters
Estimated VRAM Required
68.31
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 20.3 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 (Q6_K_XL) 65 GB
Context Overhead (8,192 tokens × 80B × 2 ÷ 1M) 1.311 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 68.31 GB

Try a Different Quantization

Use the interactive calculator to compare Qwen3-Next-80B-A3B-Instruct-GGUF across all available formats.

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Qwen3-Next-80B-A3B-Instruct-GGUF — Other Quantizations

Q2_K 25 GB file
28.31 GB
Q2_K_L 25 GB file
28.31 GB
Q2_K_XL 25 GB file
28.31 GB
Q2_XXS 25 GB file
28.31 GB
Q3_K_M 35 GB file
38.31 GB
Q3_K_S 35 GB file
38.31 GB
Q3_K_XL 35 GB file
38.31 GB
Q3_XXS 35 GB file
38.31 GB
Q4_K_XL 45 GB file
48.31 GB
Q4_K_S 45 GB file
48.31 GB
Q4_K_M 45 GB file
48.31 GB
Q4_1 45 GB file
48.31 GB
Q4_0 45 GB file
48.31 GB
Q4_NL 45 GB file
48.31 GB
Q4_XS 45 GB file
48.31 GB
Q5_K_M 55 GB file
58.31 GB
Q5_K_S 55 GB file
58.31 GB
Q5_K_XL 55 GB file
58.31 GB
Q6_K 65 GB file
68.31 GB
Q6_K_XL Current
68.31 GB
Q1_0 80 GB file
83.31 GB
Q1_S 80 GB file
83.31 GB
Q1_M 80 GB file
83.31 GB
Q8_0 85 GB file
88.31 GB
Q8_K_XL 85 GB file
88.31 GB
BF16 160 GB file
163.31 GB
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Frequently Asked Questions

Can I run Qwen3-Next-80B-A3B-Instruct-GGUF Q6_K_XL on a consumer GPU?
Running Qwen3-Next-80B-A3B-Instruct-GGUF Q6_K_XL locally requires 68.31 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-Next-80B-A3B-Instruct-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_XL quality good enough for production?
Q6_K_XL is suitable for specialized use cases. Check community benchmarks for specific quality metrics.