Baidu's Ernie 4.5 represents a significant step forward in publicly available large-scale models. The series includes a diverse set of 10 models, covering both text-only (LLM) and vision-language (VL) tasks. The models are available on platforms like Hugging Face and AI Studio under a permissive Apache 2.0 license, making them accessible for both research and commercial use.

The highlight innovation in Ernie 4.5 is the use of a heterogeneous Mixture-of-Experts (MoE) architecture in its larger variants. This design enables the sharing of certain parameters across different data modalities (such as text, image, and video), while others remain dedicated. This approach enhances multimodal reasoning without degrading performance on text-specific tasks. The largest models in the family have up to 424 billion total parameters, though only a fraction of these, the "activated" parameters, are used during inference, making the MoE architecture highly efficient.

The Ernie 4.5 family was trained using Baidu's own PaddlePaddle deep learning framework and includes supporting toolkits, such as ERNIEKit and FastDeploy, to simplify fine-tuning and deployment.

Heterogeneous Mixture-of-Experts (MoE)

This diagram show how a heterogeneous MoE model, such as Ernie 4.5, might function. The router directs different types of data to a mix of shared and specialized experts, combining their outputs to achieve a more comprehensive result.

GPU Requirement Guide for Ernie 4.5

To run any Ernie 4.5 model, your GPU's Video RAM (VRAM) is the most important factor. VRAM must hold the model's parameters, input data, and intermediate calculations. Vision-Language Models must load the entire architecture into memory to process any modality, so the VRAM requirement is the same for text, image, or video inputs.

Below are tables detailing the estimated VRAM for each model using FP16 (half-precision) and INT4 (4-bit quantization).

VRAM Requirements for FP16 (Base) Inference

This precision strikes a good balance between performance and accuracy, serving as a reliable baseline for planning your hardware.

Model Variant	Parameters	Required VRAM	Recommended GPU(s)
ERNIE-4.5-VL-424B	424B (47B active)	~945 GB	NVIDIA H100 (80GB) x12
ERNIE-4.5-300B	300B (47B active)	~668 GB	NVIDIA H100 (80GB) x9
ERNIE-4.5-VL-28B	28B (3B active)	~64 GB	NVIDIA A100/H100 (80GB)
ERNIE-4.5-21B	21B (3B active)	~48 GB	NVIDIA RTX 6000 Ada (48GB)
ERNIE-4.5-0.3B	300M	~2.5 GB	NVIDIA RTX 4060 (8GB) / RTX 3060 (12GB)

VRAM Requirements for INT4 Quantized Inference

Quantization significantly reduces memory requirements, making models accessible on a wider range of hardware or enabling higher throughput.

Model Variant	Parameters	Required VRAM	Recommended GPU(s)
ERNIE-4.5-VL-424B	424B (47B active)	~237 GB	NVIDIA H100 (80GB) x3
ERNIE-4.5-300B	300B (47B active)	~168 GB	NVIDIA H100 (80GB) x3
ERNIE-4.5-VL-28B	28B (3B active)	~17 GB	NVIDIA RTX 4090 (24GB) / A10G (24GB)
ERNIE-4.5-21B	21B (3B active)	~13 GB	NVIDIA RTX 4080 (16GB) / A10G (24GB)
ERNIE-4.5-0.3B	300M	~1.8 GB	Most GPUs with >4GB VRAM

Use Cases for Each Ernie 4.5 Model

Different model sizes are suited for other tasks. Here’s a breakdown of practical applications for the Ernie 4.5 series.

ERNIE-4.5 300B & 424B Series (LLM & VLM)

These are flagship models designed for maximum performance and complex reasoning.

Use Case Category	Specific Applications
Advanced Content Creation	Generating long-form, high-quality articles, technical reports, and creative fiction.
Scientific Research	Assisting in complex problem-solving by analyzing research papers and interpreting diagrams or medical imagery.
Interactive Entertainment	Driving sophisticated non-player characters (NPCs) in games that can react to both text and visual cues.

ERNIE-4.5 21B & 28B Series (LLM & VLM)

These models offer a strong balance of performance and resource efficiency, making them ideal for enterprise applications.

Use Case Category	Specific Applications
Enterprise Chatbots & Virtual Assistants	Powering customer service bots that can handle complex queries and summarize documents.
Document Intelligence	Extracting structured information from unstructured documents, forms, and invoices that contain both text and images.
Personalized Marketing	Generating targeted marketing copy and creating dynamic visual content for ads.

ERNIE-4.5 0.3B (Dense)

This small, efficient model is designed for high-throughput or edge-computing scenarios.

Use Case Category	Specific Applications
Text Classification	Performing tasks like sentiment analysis, topic categorization, or spam detection at scale.
On-Device AI	Running directly on mobile devices for real-time applications like text completion or simple Q&A.
Lightweight Summarization	Creating quick summaries of emails or articles for fast review.

How to Set Up Your Environment for Large Models

Once you have the hardware, you can use libraries like transformers to run an Ernie 4.5 model. This code gives a simplified example of loading the 21B model.

Python Code Example: Loading a Model

This snippet demonstrates loading the model and tokenizer using the Hugging Face transformers library.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Define the model identifier from Hugging Face
model_id = "baidu/ERNIE-4.5-21B-A3B-Base"

# Select the torch data type based on your GPU capability
# Use torch.float16 or torch.bfloat16 for supported GPUs
dtype = torch.bfloat16

# Set the device to load the model onto
device = "cuda"

print(f"Loading model: {model_id}")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load the model with appropriate settings
# device_map="auto" helps distribute layers across multiple GPUs
model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=dtype,
  device_map="auto",
  trust_remote_code=True # Required for custom architectures
)

print("Model loaded successfully on GPU.")

# Example prompt
prompt = "What are the key innovations in the Ernie 4.5 model series?"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

# Generate text
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

A Note on Multi-GPU Inference

For models like the ERNIE-4.5-300B, you must use a parallelization strategy to split the model across multiple GPUs. A common approach is Tensor Parallelism, where the model's weight matrices are sharded across several GPUs. Each GPU holds a slice of the model and computes its portion, with the results synchronized over a high-speed interconnect, such as NVLink.

Optimizing Your Inference Setup

Choose the Right Quantization Level: As shown, quantization (INT8 or INT4) can cut VRAM usage by 50-75%. It's the most effective way to run large models on smaller hardware.
Leverage Flash Attention: Use a Flash Attention implementation for supported models. This optimizes attention calculations, resulting in significant speedups and reduced memory usage.
Implement Batching Strategically: Grouping requests into batches increases GPU utilization. However, it also increases VRAM usage, so find the sweet spot your hardware can handle.
Utilise Optimised Inference Servers: Tools such as vLLM, TensorRT-LLM, or Baidu's FastDeploy are specifically designed for high-performance inference, which can significantly improve tokens per second.

Conclusion

The Ernie 4.5 family from Baidu offers powerful new tools for developers, but comes with demanding hardware requirements for all but the smallest models. A high-end enterprise GPU with at least 48GB of VRAM is recommended. The largest models in the series make multi-GPU servers an absolute necessity.

Understanding the trade-offs between model size, precision, and the specific task is fundamental to building a cost-effective and performant inference setup. By utilizing optimization strategies such as quantization and specialized inference servers, you can effectively deploy these state-of-the-art models to develop the next generation of AI applications.

GPU System Requirement Guide for Baidu Ernie 4.5