ERNIE-4.5-300B-A47B
Active Parameters
300B
Context Length
131K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
Apache 2.0
Release Date
30 Jun 2025
Knowledge Cutoff
Mar 2025
API Pricing (per 1M)
Input: $0.28 · Output: $1.10
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
34x RTX 4090
24GB VRAM
Datacenter
10x NVIDIA A100
80GB VRAM
Apple Silicon
7x Apple M3 Max
128GB VRAM
131,072 tokens
Consumer
36x RTX 4090
24GB VRAM
Datacenter
10x NVIDIA A100
80GB VRAM
Apple Silicon
8x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#184
| Benchmark | Score | Rank |
|---|---|---|
Intelligence Index | 0.07 | 212 |
Rankings
Overall Rank
#184
Coding Rank
-
About ERNIE-4.5-300B-A47B
ERNIE-4.5-300B is Baidu's large-scale text Mixture-of-Experts model activating 47B parameters for advanced English and Chinese natural language reasoning. It supports 4-bit and 2-bit quantization for enterprise-grade deployment over a 131K context.
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
64
Key-Value Heads
8
Attention Head Dimension
-
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
Swish
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
8,192
Number of Layers
54
FFN Intermediate Size (Dense)
3,584
Multi-Token Prediction Heads
1
Tokenizer
Vocabulary Size
103,424
Mixture of Experts
Total Expert Parameters
47.0B
Number of Experts
64
Active Experts
8
Shared Experts
0
FFN Intermediate Size (per Expert)
3,584
Dense Layers Before MoE
3
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.