ERNIE-4.5-21B-A3B-PT
ERNIE-4.5-21B-A3B-PT is a 21-billion-parameter mixture-of-experts (MoE) language model from Baidu that activates only 3 billion parameters per token. It supports English and Chinese, runs on PyTorch, handles 131k-token context, and is available under Apache 2.0 without gating. Suitable for self-hosted deployment and custom LLM applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | baidu |
| Parameters | 21.9B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 66.2k |
| Likes | 176 |
| Last updated | 2025-11-26 |
| Source | baidu/ERNIE-4.5-21B-A3B-PT |
What ERNIE-4.5-21B-A3B-PT is
Text-only MoE model with 21B total parameters, 3B activated per token, 28 layers, 20 query/4 key-value heads, 64 text experts (6 activated), 2 shared experts, and 131,072 token context. Post-trained with SFT/DPO/UPO. PyTorch weights compatible with transformers>=4.54.0. Supports bfloat16 precision and vLLM>=0.10.2 serving.
Run ERNIE-4.5-21B-A3B-PT locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="baidu/ERNIE-4.5-21B-A3B-PT")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
**ESTIMATE (requires verification)**. Unquantized bfloat16: ~42–44 GB VRAM (21B × 2 bytes). With 4-bit quantization: ~11–13 GB VRAM. Single A100 (80GB), H100, or dual A6000 (48GB each) recommended for production. vLLM and PaddlePaddle support optimizations (FP8 mixed-precision, convolutional code quantization, multi-expert parallel collaboration) claimed but benchmarks unavailable.
LoRA/QLoRA feasible given 21B base size and post-training lineage (SFT/DPO/UPO mentioned). Card does not explicitly document LoRA rank recommendations or quantization + LoRA compatibility. Requires testing; PaddlePaddle ecosystem support may differ from transformers/PEFT standard tooling.
When to avoid it — and what to weigh
- Vision/Multimodal Tasks Required — This model is text-only. The card mentions multimodal training architecture but this specific checkpoint (-PT) is text modality only. Requires separate VLM variant for image understanding.
- Extremely Low Latency (<50ms) Needed — MoE routing overhead and 131k context support add latency vs. smaller dense models. Verify serving infrastructure and quantization impact before latency-critical SLAs.
- Closed-Source Production Compliance — Model does not ship with formal security audit, compliance certifications, or SLAs. Open-source nature requires internal validation for regulated industries (healthcare, finance).
- GPU Memory Severely Constrained (<24GB) — 21B parameters at bfloat16 requires ~42GB VRAM unquantized. Even with 4-bit quantization, serving multiple concurrent requests may exceed mid-range GPU memory budgets.
License & commercial use
Apache License 2.0. Permits use, modification, and distribution subject to license terms. Copyright Baidu 2025.
Apache 2.0 is a permissive OSI-approved license that explicitly permits commercial use. Model card states: 'This license permits commercial use, subject to its terms and conditions.' No gating, no login required. However: (1) internal production security/compliance validation recommended before regulated/mission-critical deployment; (2) no SLA, support, or liability protection from Baidu; (3) modifications and derived models must include Apache 2.0 attribution.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No formal security audit, threat model, or adversarial robustness evaluation provided. Open-source code on GitHub (PaddlePaddle/ERNIE) allows community review but no guarantee of vulnerability disclosure timeline. Pre-training data sources and curation not disclosed—risks of data leakage or unwanted biases. Deployment in air-gapped environments recommended for sensitive workloads. Validate outputs for hallucinations and off-topic generation before production.
Alternatives to consider
Qwen 2.5 (7B/14B/32B)
Alibaba's dense models with multilingual support, Apache 2.0, strong community. Easier serving/fine-tuning pipeline; no MoE complexity. Trade-off: higher per-token latency for equivalent capability tier.
Mistral 7B or Mixtral 8x7B
Open-source, Apache 2.0, proven production stability. Mixtral offers MoE efficiency; Mistral is denser. Less multilingual (limited Chinese support); smaller context window.
LLaMA 3.1 (8B/70B/405B)
Meta's widely-adopted, strong community. Llama 3.1 70B offers high capability; however, custom license (not Apache 2.0) requires review for commercial use, and context length 128k matches ERNIE 4.5.
Ship ERNIE-4.5-21B-A3B-PT with senior software developers
Evaluate this open-source model in your infrastructure. Download from Hugging Face, integrate with vLLM or transformers, and validate performance on your use case. Need guidance on quantization, fine-tuning, or compliance? Let's architect your deployment.
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ERNIE-4.5-21B-A3B-PT FAQ
Can I use ERNIE-4.5-21B-A3B-PT commercially without paying Baidu?
What is the minimum GPU memory I need to run this model?
Does this model support vision/image inputs?
Can I fine-tune this with LoRA?
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Deploy ERNIE-4.5-21B for Your LLM Workload
Evaluate this open-source model in your infrastructure. Download from Hugging Face, integrate with vLLM or transformers, and validate performance on your use case. Need guidance on quantization, fine-tuning, or compliance? Let's architect your deployment.