Zdchen FP16 21B Parameters

VRAM Requirements for
ERNIE-4.5-21B-A3B-PT-Q4_K_M-GGUF FP16

To run ERNIE-4.5-21B-A3B-PT-Q4_K_M-GGUF locally at FP16 quantization, you need at minimum 44.34 GB of GPU VRAM.

44.34 GB
Required VRAM
42 GB
File Size
8K tokens
Context Window
21B
Parameters
Estimated VRAM Required
44.34
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

⚠️ 4.3 GB short
Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (FP16) 42 GB
Context Overhead (8,192 tokens × 21B × 2 ÷ 1M) 0.344 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 44.34 GB

Try a Different Quantization

Use the interactive calculator to compare ERNIE-4.5-21B-A3B-PT-Q4_K_M-GGUF across all available formats.

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ERNIE-4.5-21B-A3B-PT-Q4_K_M-GGUF — Other Quantizations

Q4_0 11.81 GB file
14.15 GB
FP16 Current
44.34 GB
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Frequently Asked Questions

Can I run ERNIE-4.5-21B-A3B-PT-Q4_K_M-GGUF FP16 on a consumer GPU?
Running ERNIE-4.5-21B-A3B-PT-Q4_K_M-GGUF FP16 locally requires 44.34 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-4.5-21B-A3B-PT-Q4_K_M-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 FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.