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ERNIE-4.5-300B-A47B-Base

Active Parameters

300B

Context Length

131K

Modality

Text

Architecture

Mixture of Experts (MoE)

License

Apache 2.0

Release Date

30 Jun 2025

Knowledge Cutoff

Jun 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

631.74 GB VRAM

Consumer

34x RTX 4090

24GB VRAM

Datacenter

10x NVIDIA A100

80GB VRAM

Apple Silicon

7x Apple M3 Max

128GB VRAM

131,072 tokens

661.94 GB VRAM

Consumer

36x RTX 4090

24GB VRAM

Datacenter

10x NVIDIA A100

80GB VRAM

Apple Silicon

8x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 12.3k · Context: 131K · Vocab: 103.4kx 54 layersLayerNormPre-AttentionGrouped-Query Attention64Q / 8KV headsHead dim: 192+LayerNormPre-FFNSparse MoE FFN (8/64 experts)GELUIntermediate: 3.6k+Final LayerNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for ERNIE-4.5-300B-A47B-Base available.

Rankings

Overall Rank

-

Coding Rank

-

About ERNIE-4.5-300B-A47B-Base

ERNIE-4.5-300B-Base is a 300B text Mixture-of-Experts base model from Baidu activating 47B parameters for large-scale bilingual pretraining. It incorporates intra-node expert parallelism and Grouped-Query Attention across a 131K context window.

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

Layer Normalization

Activation Function

GELU

Dimensions

Hidden Dimension Size

12,288

Number of Layers

54

FFN Intermediate Size (Dense)

3,584

Multi-Token Prediction Heads

-

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.


Other ERNIE 4.5 Models