Open-Weight LLM · Private & Custom AI
OTel-LLM-E4B-IT
A 4.5B telecom-specialized LLM fine-tuned for context-grounded RAG pipelines—deploy privately to automate internal telecom ops workflows without exposing domain queries to third parties.
OTel-LLM-E4B-IT is a Apache 2.0 open-weight model derived from Google's Gemma-4-E4B-IT, fine-tuned on 326K curated telecom QA examples (standards, O-RAN specs, 3GPP docs, GSMA references). It improves context-grounded correctness +9.3pp over base on OTel's held-out test set. For ops teams in telecom or network-adjacent sectors, it enables private RAG systems that answer domain-specific technical questions without vendor lock-in or data egress.
Model facts
Private deployment
Run OTel-LLM-E4B-IT in your own environment
4.5B params fit comfortably on a single GPU (≈9–12 GB VRAM in BF16, estimated ~6–8 GB in quantized int8). Run it self-hosted in your VPC, on-premise, or air-gapped: retrieve internal telecom docs/RFCs/PRDs, feed context to OTel-LLM-E4B-IT, keep all queries and responses inside your environment. No model callback home, no third-party training or logging. Your data never leaves the deployment boundary.
Operational AI use cases
Internal Telecom Support Automation
Route internal employee questions ("What is the F1 interface in O-RAN?" / "How do we configure MBSFN?") to a RAG pipeline backed by OTel-LLM-E4B-IT, retriever pulling from your 3GPP docs, RFCs, and vendor specs. Reduces tier-1 support load; engineers get instant context-grounded answers without waiting for a colleague.
Network Operations Knowledge Base Agent
Index your runbooks, configuration guides, incident postmortems, and telecom standards into a vector DB. When NOC staff ask operational questions, OTel-LLM-E4B-IT synthesizes answers from that context—faster onboarding, fewer escalations, consistent guidance across shifts.
Compliance & Standards Documentation Lookup
Auto-answer domain questions about GSMA PRDs, O-RAN working-group outputs, IETF RFC requirements, and internal policy. Legal/ops teams query privately; the model surfaces relevant standards passages and compliance mappings without external API calls or audit trails on public services.
Custom AI
As a base for custom AI
Use OTel-LLM-E4B-IT as a foundation to build a vertically integrated telecom or mobile-network product. The model already speaks domain language (O-RAN, 3GPP, GSMA, IETF). You can further fine-tune it on your proprietary network logs, vendor APIs, or internal standards—full-parameter tuning is available. Package it as an on-prem or SaaS knowledge engine that your customers run privately, or as a white-label RAG endpoint.
In the operating system
Where it fits
Sits in the **generation layer** of an ops AI stack. Upstream: retriever (vector DB + reranker—OTel publishes a reranker variant). Downstream: your business logic (agent orchestration, guardrails, fallback routing). In a full LLM.co system, OTel-LLM-E4B-IT is the LLM backbone for a telecom-ops agent that reads docs, answers questions, and can trigger workflows if integrated with orchestration.
Data control & security
Self-hosting ensures all prompts, retrieved context, and completions remain in your infrastructure. No data leaves for model inference, analytics, or retraining. You own the model weights (Apache 2.0), can audit the training data sources (published on HuggingFace), and control access via your deployment perimeter. Note: security/compliance certification is your responsibility; the model itself is not inherently HIPAA/SOC2—your infrastructure must enforce those controls.
Hardware footprint
Estimated ~9–12 GB VRAM (BF16, 4.5B params); ~6–8 GB quantized (int8). Single A100 40GB, H100 80GB, or MI300X sufficient. For batch processing, parallel decoding across 2–4 GPUs on larger clusters. CPU-only inference possible but slow (~500ms per 128-token output, unoptimized); GPU strongly recommended for sub-second latency.
Integration
Drop into any Hugging Face Transformers pipeline (AutoModelForCausalLM, AutoTokenizer). Supports BF16 (recommended) and int8 quantization. Integrate with LangChain/LlamaIndex for RAG, or build a custom FastAPI wrapper calling model.generate(). Connect to your vector DB (Milvus, Pinecone on-prem, Weaviate) and retriever. Pair with OTel's published reranker for ranking context. Expose via HTTP if integrating with ops dashboards or chatbots; containerize (Docker) for Kubernetes or on-prem VMs.
When it's not the right fit
- —Your use case requires **unrestricted, context-free telecom QA** (model is optimized for RAG; will hallucinate without grounding context).
- —You need **non-English or multilingual** telecom assistance (model trained English-only; cross-lingual behavior untested).
- —You require **real-time network telemetry analysis or live streaming data** (OTel-LLM-E4B-IT is a text-generation LLM, not a time-series forecasting or anomaly-detection model).
- —Your domain is **outside telecom** (finance, healthcare, general knowledge)—performance on non-telecom text is unknown; use a general-purpose model or domain-specific alternative.
Alternatives to consider
Llama 2 / Llama 3 (Meta)
Larger (7B–70B), general-purpose, well-established. Requires more VRAM; no telecom specialization but stronger on general knowledge and code. Better for mixed workloads; worse for depth in standards/RFCs.
Mistral 7B / Mixtral 8x7B
Efficient, open-weight, good instruction-following. Not domain-tuned; you'd need to fine-tune heavily on telecom data yourself. Smaller context window than OTel baseline in some configs.
Microsoft Phi-4 / PhiLM (domain variants)
Compact, low-VRAM models with some domain variants. Less established telecom fine-tuning; fewer academic/standards data sources than OTel's 326K curated examples.
Related open models
FAQ
Can I run OTel-LLM-E4B-IT completely offline/air-gapped?
Yes. Download the model weights from HuggingFace once, load locally, and never call home. All inference stays in your environment. Retrieval, generation, and context all on-prem.
Is this model free for commercial use?
Yes, under Apache 2.0. You can build products, sell services, fine-tune for customers, and distribute modified versions—so long as you include the Apache 2.0 license and attribute the OTel project. Review the base model (google/gemma-4-E4B-IT) license separately; it is also permissive.
What if my retrieved context is incomplete or irrelevant?
The model is optimized for *grounded* answering and may hallucinate without good context. Use an abstention-aware prompt (model was trained with `abstention` labels) or the `-Safety` variant if available. Always validate generated telecom answers before ops/regulatory use.
Can I fine-tune this model on my company's proprietary telecom data?
Yes. Apache 2.0 permits full-parameter fine-tuning. The model card mentions the training recipe (AdamW, BF16, Flash Attention 2, Fully Sharded Data Parallel). You'll need GPU compute and your own data; keep fine-tuned weights proprietary if desired.
Build a Private Telecom AI System with LLM.co
OTel-LLM-E4B-IT is ready to deploy in your VPC. Connect it to your ops workflows, internal docs, and domain knowledge base—no cloud APIs, no vendor lock-in. Let LLM.co help you architect a custom private-AI system for telecom ops. Talk to our team.