Xythic K Q4_K_S 21B Parameters

VRAM Requirements for
ERNIE-4.5-21B-A3B-Thinking-GGUF Q4_K_S

To run ERNIE-4.5-21B-A3B-Thinking-GGUF locally at Q4_K_S quantization, you need at minimum 14.15 GB of GPU VRAM.

14.15 GB
Required VRAM
11.81 GB
File Size
8K tokens
Context Window
21B
Parameters
Estimated VRAM Required
14.15
GB
Mid-Range GPU Required
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 (Q4_K_S) 11.81 GB
Context Overhead (8,192 tokens × 21B × 2 ÷ 1M) 0.344 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 14.15 GB

Try a Different Quantization

Use the interactive calculator to compare ERNIE-4.5-21B-A3B-Thinking-GGUF across all available formats.

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ERNIE-4.5-21B-A3B-Thinking-GGUF — Other Quantizations

Q2_K 6.56 GB file
8.9 GB
Q2_K_L 6.56 GB file
8.9 GB
Q2_K_XL 6.56 GB file
8.9 GB
Q2_M 6.56 GB file
8.9 GB
Q2_XXS 6.56 GB file
8.9 GB
Q3_K_M 9.19 GB file
11.53 GB
Q3_K_S 9.19 GB file
11.53 GB
Q3_K_XL 9.19 GB file
11.53 GB
Q3_XXS 9.19 GB file
11.53 GB
Q4_XS 11.81 GB file
14.15 GB
Q4_NL 11.81 GB file
14.15 GB
Q4_K_S Current
14.15 GB
Q4_K_M 11.81 GB file
14.15 GB
Q4_1 11.81 GB file
14.15 GB
Q4_0 11.81 GB file
14.15 GB
Q5_K_M 14.44 GB file
16.78 GB
Q6_K 17.06 GB file
19.4 GB
Q1_M 21 GB file
23.34 GB
Q1_S 21 GB file
23.34 GB
Q8_0 22.31 GB file
24.65 GB
BF16 42 GB file
44.34 GB
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Frequently Asked Questions

Can I run ERNIE-4.5-21B-A3B-Thinking-GGUF Q4_K_S on a consumer GPU?
Yes! At 14.15 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 ERNIE-4.5-21B-A3B-Thinking-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_K_S 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.