ERNIE-4.5-21B-A3B-Base
Active Parameters
21B
Context Length
131K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
Apache 2.0
Release Date
30 Jun 2025
Knowledge Cutoff
Dec 2024
API Pricing (per 1M)
Self-hosted only
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
2x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
131,072 tokens
Consumer
3x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for ERNIE-4.5-21B-A3B-Base available.
Rankings
Overall Rank
-
Coding Rank
-
About ERNIE-4.5-21B-A3B-Base
ERNIE-4.5-21B-Base is an open-weights Mixture-of-Experts text model by Baidu activating 3B parameters per token for efficient NLP applications. It utilizes modality-isolated routing and progressive RoPE scaling across a 131K context window.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
20
Key-Value Heads
4
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
500,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
2,560
Number of Layers
28
FFN Intermediate Size (Dense)
1,536
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
103,424
Mixture of Experts
Total Expert Parameters
3.0B
Number of Experts
64
Active Experts
6
Shared Experts
2
FFN Intermediate Size (per Expert)
1,536
Dense Layers Before MoE
1
About ERNIE 4.5
The Baidu ERNIE 4.5 family consists of ten large-scale multimodal models. They utilize a heterogeneous Mixture-of-Experts (MoE) architecture, which enables parameter sharing across modalities while also employing dedicated parameters for specific modalities, supporting efficient language and multimodal processing.