Open-Weight LLM · Private & Custom AI
ERNIE-4.5-21B-A3B-PT
MoE text model (21B total, 3B active) for private deployment in ops automation, document processing, and internal knowledge workflows where data residency and cost efficiency matter.
ERNIE-4.5-21B-A3B-PT is a Mixture-of-Experts language model from Baidu with 131k context length, Apache 2.0 licensed, optimized for both Chinese and English. It trades off raw capability for efficient inference—only 3B parameters activate per token—making it viable for private infrastructure without the resource overhead of dense 21B models. Operations teams use it to automate support ticket triage, document classification, and internal Q&A without shipping data to external APIs.
Model facts
Private deployment
Run ERNIE-4.5-21B-A3B-PT in your own environment
Self-hosting is straightforward: the model runs on PyTorch (transformers 4.54.0+) or vLLM on a single GPU (estimate: 24–32 GB VRAM in bfloat16, less with 4-bit quantization per Baidu's lossless techniques). Data stays in your environment—no vendor lock, no API logs. Trade-off: you own inference latency tuning, serving complexity, and model updates. Ideal for companies with moderate query volume (<10k requests/day) that require strict data isolation or cannot accept external SaaS terms.
Operational AI use cases
Support Ticket Routing & Triage
Classify incoming tickets by urgency, department, and issue type. Use the model's chat template and 131k context to read full ticket histories without truncation. Route to the right team automatically, reduce manual sorting overhead by 40–60%. Long context lets you include SOP docs and FAQs inline for routing logic.
Internal Knowledge Base Q&A
Deploy as a chatbot over internal docs (policies, onboarding, compliance manuals, run-books). Employees query in natural language; the model retrieves and synthesizes answers from your knowledge base without exposing it externally. Reduce HR/IT support requests by surfacing self-service answers first. 131k context accommodates large doc sets per session.
Financial & HR Document Processing
Extract structured data from invoices, expense reports, offer letters, and contracts. Chain the model with regex/parsing for entity extraction (dates, amounts, signatory names). Batch-process hundreds of documents per hour on private infrastructure. Audit trail stays internal; no third-party data residency concerns.
Custom AI
As a base for custom AI
Strong foundation for proprietary applications: fine-tune on your ops workflows (ticket resolution steps, internal style guides, domain jargon) or use as a backbone for an agentic system (e.g., HR agent that queries your payroll API, compliance agent that cross-checks policy docs). The MoE architecture's modular expert structure is less explored than dense models in the open literature, so custom tuning requires some experimentation. Apache 2.0 license permits commercial rebranding and derivative products.
In the operating system
Where it fits
**Knowledge layer**: Sits behind vector embeddings and retrieval pipelines (feeds retrieved docs into the model for synthesis). **Workflow layer**: Endpoint for ops agents—classifies, extracts, and proposes actions. **Not ideal for**: Real-time decision-making (inference latency on CPU is ~500–2000ms per token; GPU reduces it, but still not sub-100ms). Better for batch and asynchronous workflows.
Data control & security
Private deployment architecture means all prompts, documents, and model outputs remain in your network—no third-party logging or model training on your data. Baidu does not access inference traffic. Compliance benefits: GDPR/HIPAA feasibility (your responsibility to audit and apply encryption, access controls, etc.; the model itself does not enforce these). **No claims**: the model is not inherently 'secure'—security is a deployment design choice. Monitor model outputs for unintended data leakage in generated text.
Hardware footprint
**Estimate (bfloat16)**: 24–32 GB VRAM for single-model serving. **With 4-bit quantization** (Baidu's lossless technique referenced in model card): 8–12 GB VRAM. **CPU inference**: feasible but slow (~5–30 tokens/sec); GPU recommended for <500ms latency targets. Batch inference on modest hardware (1× A6000 or H100) handles 100–500 concurrent users at <2s P95 latency, depending on sequence length.
Integration
Use `transformers` library (v4.54.0+) with `device_map='auto'` for multi-GPU scaling. vLLM (≥0.10.2, excluding 0.11.0) for high-concurrency serving. Integrates with FastAPI / Flask for REST endpoints. For ops workflows: wrap around Zapier, Make, or native APIs (Jira, Salesforce, etc.). Chat template baked into tokenizer simplifies multi-turn conversations. Expects bfloat16 or int8 quantization for stability; test before production. Deploy on Docker + Kubernetes for isolation and scaling.
When it's not the right fit
- —You need real-time, sub-100ms inference latency across all requests—MoE overhead and model size make this challenging even on H100s. Use a smaller dense model or a speculative decoding setup.
- —Your ops workflow requires high-reliability structured output (JSON, XML)—the model's output generation is probabilistic. Pair with a strict output validator and retry logic, or use smaller fine-tuned models trained for slot-filling.
- —You need visual understanding—this model is text-only. Baidu publishes VLM variants (A47B) separately; evaluate those if image/document understanding is required.
- —Your team lacks GPU infrastructure and bandwidth to self-host—inference on CPU is slow and expensive. Consider API access or a smaller model.
Alternatives to consider
Meta Llama 3.1 (70B / 8B)
Dense, well-supported, larger model for higher accuracy but no MoE efficiency gain. Better community ecosystem; more tuning examples available. No context-length advantage.
Alibaba Qwen2.5 (32B / 14B)
Similar bilingual support (ZH/EN), strong on ops tasks (document QA, classification). Not MoE, so predictable resource use but less inference efficiency. Strong Chinese reasoning; comparable licensing (Apache 2.0).
Google Gemma 2 (27B / 9B)
Slightly larger dense alternative, Gemma license (commercial-friendly). No MoE, no 131k context. Simpler to fine-tune; worse on Chinese; strong English reasoning and open-source community.
Related open models
FAQ
Can I run this entirely on-premises without any cloud dependency?
Yes. Download weights from HuggingFace, run on your hardware with transformers or vLLM. No callbacks to Baidu or external services. You manage the server, networking, and updates. Suitable for air-gapped environments if you pre-download dependencies.
Is this model commercially usable, and do I need permission from Baidu?
Yes, Apache 2.0 license permits commercial use without requesting permission. You may build products, resell services, and redistribute derivatives as long as you retain the Apache 2.0 license and copyright notice. No royalties or approval gates.
How does the MoE architecture affect fine-tuning costs?
Unknown from public data. MoE fine-tuning is less explored than dense model tuning. Baidu likely uses expert-selective fine-tuning to avoid training all 64 experts. Plan for 1.5–2× the compute cost of a comparable 3B dense model during tuning; validate with Baidu's GitHub or technical report for specifics.
What's the actual latency for a typical ops task (e.g., ticket classification)?
Rough estimates: GPU (A100/H100): 200–500ms for 64 tokens. CPU: 2–5 seconds. Depends on batch size, sequence length, and quantization. Measure on your target hardware before committing; MoE activation adds modest overhead vs. dense models.
Build a Private Ops AI System with ERNIE 4.5
LLM.co helps you self-host ERNIE-4.5-21B and connect it to your workflows—ticket routing, HR docs, knowledge bases—without shipping data outside your environment. Let's architect a custom AI operating system for your ops stack. Start a free consultation.