Unsloth Q8_K_XL 480B Parameters

VRAM Requirements for
Qwen3-Coder-480B-A35B-Instruct-1M-GGUF Q8_K_XL

To run Qwen3-Coder-480B-A35B-Instruct-1M-GGUF locally at Q8_K_XL quantization, you need at minimum 519.86 GB of GPU VRAM.

519.86 GB
Required VRAM
510 GB
File Size
8K tokens
Context Window
480B
Parameters
Estimated VRAM Required
519.86
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 359.9 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 359.9 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 (Q8_K_XL) 510 GB
Context Overhead (8,192 tokens × 480B × 2 ÷ 1M) 7.864 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 519.86 GB

Try a Different Quantization

Use the interactive calculator to compare Qwen3-Coder-480B-A35B-Instruct-1M-GGUF across all available formats.

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Qwen3-Coder-480B-A35B-Instruct-1M-GGUF — Other Quantizations

Q2_K 150 GB file
159.86 GB
Q2_K_L 150 GB file
159.86 GB
Q2_K_XL 150 GB file
159.86 GB
Q3_K_M 210 GB file
219.86 GB
Q3_K_S 210 GB file
219.86 GB
Q3_K_XL 210 GB file
219.86 GB
Q3_XXS 210 GB file
219.86 GB
Q4_0 270 GB file
279.86 GB
Q4_1 270 GB file
279.86 GB
Q4_K_M 270 GB file
279.86 GB
Q4_K_S 270 GB file
279.86 GB
Q4_K_XL 270 GB file
279.86 GB
Q4_NL 270 GB file
279.86 GB
Q4_XS 270 GB file
279.86 GB
Q5_K_S 330 GB file
339.86 GB
Q5_K_M 330 GB file
339.86 GB
Q5_K_XL 330 GB file
339.86 GB
Q6_K 390 GB file
399.86 GB
Q6_K_XL 390 GB file
399.86 GB
Q1_M 480 GB file
489.86 GB
Q8_0 510 GB file
519.86 GB
Q8_K_XL Current
519.86 GB
BF16 960 GB file
969.86 GB
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Frequently Asked Questions

Can I run Qwen3-Coder-480B-A35B-Instruct-1M-GGUF Q8_K_XL on a consumer GPU?
Running Qwen3-Coder-480B-A35B-Instruct-1M-GGUF Q8_K_XL locally requires 519.86 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-Coder-480B-A35B-Instruct-1M-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 Q8_K_XL quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.