Unsloth Q4_0 300B Parameters

VRAM Requirements for
ERNIE-4.5-300B-A47B-PT-GGUF Q4_0

To run ERNIE-4.5-300B-A47B-PT-GGUF locally at Q4_0 quantization, you need at minimum 175.67 GB of GPU VRAM.

175.67 GB
Required VRAM
168.75 GB
File Size
8K tokens
Context Window
300B
Parameters
Estimated VRAM Required
175.67
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 15.7 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 15.7 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 (Q4_0) 168.75 GB
Context Overhead (8,192 tokens × 300B × 2 ÷ 1M) 4.915 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 175.67 GB

Try a Different Quantization

Use the interactive calculator to compare ERNIE-4.5-300B-A47B-PT-GGUF across all available formats.

Open Live Calculator →

ERNIE-4.5-300B-A47B-PT-GGUF — Other Quantizations

Q2_K 93.75 GB file
100.67 GB
Q2_K_L 93.75 GB file
100.67 GB
Q2_K_XL 93.75 GB file
100.67 GB
Q2_M 93.75 GB file
100.67 GB
Q2_XXS 93.75 GB file
100.67 GB
Q3_K_M 131.25 GB file
138.17 GB
Q3_K_S 131.25 GB file
138.17 GB
Q3_K_XL 131.25 GB file
138.17 GB
Q3_XXS 131.25 GB file
138.17 GB
Q4_K_XL 168.75 GB file
175.67 GB
Q4_K_S 168.75 GB file
175.67 GB
Q4_K_M 168.75 GB file
175.67 GB
Q4_1 168.75 GB file
175.67 GB
Q4_0 Current
175.67 GB
Q4_NL 168.75 GB file
175.67 GB
Q4_XS 168.75 GB file
175.67 GB
Q5_K_M 206.25 GB file
213.17 GB
Q5_K_S 206.25 GB file
213.17 GB
Q5_K_XL 206.25 GB file
213.17 GB
Q6_K 243.75 GB file
250.67 GB
Q6_K_XL 243.75 GB file
250.67 GB
Q1_0 300 GB file
306.92 GB
Q1_S 300 GB file
306.92 GB
Q1_M 300 GB file
306.92 GB
Q8_0 318.75 GB file
325.67 GB
Q8_K_XL 318.75 GB file
325.67 GB
BF16 600 GB file
606.92 GB
Advertisement Zone

Frequently Asked Questions

Can I run ERNIE-4.5-300B-A47B-PT-GGUF Q4_0 on a consumer GPU?
Running ERNIE-4.5-300B-A47B-PT-GGUF Q4_0 locally requires 175.67 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 ERNIE-4.5-300B-A47B-PT-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 Q4_0 quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.