ERNIE-4.5-VL-28B-A3B-PT

reasoning
multimodal

ERNIE-4.5-VL-28B-A3B-PT is a multimodal model from the ERNIE 4.5 family, built on a heterogeneous Mixture-of-Experts (MoE) architecture. It has 28 billion total parameters, with only 3 billion activated per inference pass, ensuring high computational efficiency. A key innovation lies in its modality-specific expert groups: separate experts handle textual and visual inputs, while shared experts and self-attention layers enable effective cross-modal interaction. The model features an adaptive vision encoder that processes images at arbitrary resolutions without distorting their aspect ratio, preserving the original proportions. For video, it employs an adaptive frame sampling strategy with temporal timestamps rendered directly onto frames, enabling precise temporal understanding. It supports a context window of up to 131,072 tokens, allowing it to handle lengthy documents and extended video sequences.

The model offers two operational modes—thinking and non-thinking—making it versatile across diverse tasks. The thinking mode enhances reasoning for complex visual challenges (e.g., STEM, mathematics, puzzles), while non-thinking mode enables rapid processing of simple, routine requests. Compared to the flagship ERNIE-4.5-VL-424B-A47B, this compact 28B-A3B variant exhibits only marginal performance degradation while drastically reducing computational requirements.

The model’s multimodal capabilities enable a wide range of practical applications: its strong performance on OCRBench (885) and DocVQA (94.1) demonstrates effectiveness in processing scanned documents, invoices, and forms; high scores on ChartQA (82.2) and TableVQA (70.0) make it suitable for analyzing charts and tables in financial and scientific data; its video understanding capabilities (MVBench 72.0, VideoMME 74.4, LongVideoBench 62.1) are valuable for security and surveillance systems; and its precise object counting (CountBench 87.6) and visual perception (RealWorldQA 69.2) can be leveraged in retail for inventory management and visual search. Released under the permissive Apache 2.0 license, the model can be freely used in commercial projects without restrictions.


Announce Date: 28.06.2025
Parameters: 29B
Experts: 130
Activated at inference: 3B
Context: 132K
Layers: 28
Attention Type: Full Attention
Developer: Baidu, Inc.
Transformers Version: 4.57.6
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore ERNIE-4.5-VL-28B-A3B-PT capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link
There are no public endpoints for this model yet.

Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended server configurations for hosting ERNIE-4.5-VL-28B-A3B-PT

Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-2.16.32.160
131,072.0
tensor
2 $0.54 1.184 Launch
teslaa2-2.16.32.160
131,072.0
tensor
2 $0.57 1.194 Launch
rtx2080ti-3.12.24.120
131,072.0
pipeline
3 $0.84 1.025 Launch
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 3.271 Launch
rtx2080ti-4.16.32.160
131,072.0
tensor
4 $1.12 2.194 Launch
rtxa5000-2.16.64.160.nvlink
131,072.0
tensor
2 $1.23 3.271 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 3.549 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 1.834 Launch
rtx3080-4.16.64.160
131,072.0
tensor
4 $1.82 1.694 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 3.539 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 8.129 Launch
h100-1.16.64.160
131,072.0
1 $3.83 8.120 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 9.948 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 18.188 Launch
h200-1.16.128.160
131,072.0
1 $4.74 16.087 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 34.104 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 1.342 Launch
teslat4-4.16.64.160
131,072.0
tensor
4 $0.96 2.367 Launch
rtxa5000-2.16.64.160.nvlink
131,072.0
tensor
2 $1.23 1.342 Launch
teslaa2-4.32.128.160
131,072.0
tensor
4 $1.26 2.389 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 1.620 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 1.610 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 6.200 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 3.668 Launch
h100-1.16.64.160
131,072.0
1 $3.83 6.191 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 8.019 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 16.259 Launch
h200-1.16.128.160
131,072.0
1 $4.74 14.158 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 32.175 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-4.16.128.160
131,072.0
tensor
4 $1.75 1.994 Launch
rtxa5000-4.16.128.160.nvlink
131,072.0
tensor
4 $2.34 1.994 Launch
teslaa100-1.16.128.160
131,072.0
1 $2.50 1.653 Launch
rtx3090-4.16.96.320
131,072.0
tensor
4 $2.97 2.552 Launch
rtx4090-4.16.96.320
131,072.0
tensor
4 $3.68 2.531 Launch
h100-1.16.128.160
131,072.0
1 $3.95 1.644 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 3.472 Launch
rtx5090-3.16.96.160
131,072.0
pipeline
3 $4.34 2.283 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 11.711 Launch
h200-1.16.128.160
131,072.0
1 $4.74 9.611 Launch
rtx5090-4.16.128.160
131,072.0
tensor
4 $5.74 6.647 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 27.627 Launch

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