Open-Weight LLM · Private & Custom AI
OTel-LLM-8B-A1B-IT
Telecom-domain LLM for private RAG pipelines: ground operational Q&A on your own 3GPP/O-RAN/GSMA knowledge without sending telecom data to third-party APIs.
OTel-LLM-8B-A1B-IT is an 8B parameter model fine-tuned on 327K curated telecom examples (3GPP, O-RAN, GSMA, IETF standards). Built on LiquidAI's LFM2.5-8B-A1B, it scores +7.2pp higher than base on context-grounded telecom QA. For ops teams in telecom carriers, vendors, and infrastructure firms, it enables private deployment of domain-specific answer generation without external API calls.
Model facts
Private deployment
Run OTel-LLM-8B-A1B-IT in your own environment
Self-hosting is the primary use case: run it on-premises or private cloud (AMD MI300X/MI325X/MI355X, NVIDIA A100/H100 recommended during training; inference fits smaller GPUs). Your company retains full control over telecom operational data—customer records, network configs, RAN specifications—staying within your environment. No data leaves for external LLM services.
Operational AI use cases
Internal Telecom Knowledge Agent
Embed in Slack/Teams or ops portal: field questions from NOC, RF engineering, and network planning teams on 3GPP procedures, O-RAN specs, and standards compliance. Context is your indexed technical library (3GPP TS docs, internal wiki, O-RAN WG PDFs). Reduces ticket hand-offs to specialist teams; accelerates onboarding.
Regulatory & Standards Compliance Assistant
Automate extraction and Q&A over GSMA PRDs, IETF RFCs, and internal compliance docs. Support/ops queries like 'What are the handover requirements per 3GPP TS 36.300?' get grounded answers without manual reference lookup. Reduce compliance review cycles.
Customer Support Escalation Triage
Pre-screen L1/L2 support tickets: match customer technical questions against internal telecom KB (SLA docs, troubleshooting guides, product specs) and suggest answers or escalation notes. Keeps sensitive customer data on-prem; reduces load on senior engineers.
Custom AI
As a base for custom AI
Ideal base for fine-tuning domain extensions: ingest your proprietary network topology docs, vendor API specs, internal runbooks, or carrier-specific standards; full-parameter fine-tuning on LFM2.5 backbone is proven (OTel training recipe uses AdamW, BF16, FSDP on commodity GPUs). Build a custom 'YourCo-LLM' for your operational workflows without starting from a general-purpose model.
In the operating system
Where it fits
RAG knowledge layer in an ops AI system: retriever (embedding model) → context ranking → OTel-LLM-8B-A1B-IT (generation) → guardrails/abstention checks. Not a general chat model; designed as the grounded answering engine for operational agents (workflow automation, ticket resolution, knowledge lookup).
Data control & security
Private self-hosting is an architecture choice: your telecom operational data (network configs, customer records, standards docs, RAN specs) never transits to external LLM APIs. You maintain direct control over model weights, inference logs, and context storage. Model itself is not 'hardened' against adversarial attack; apply standard ops security (network isolation, access controls, input validation) as you would any internal service.
Hardware footprint
Estimated VRAM (inference, per batch): ~16–20 GB (BF16 @ 8B params), ~8–12 GB (int8 quantized). Full fine-tuning (as done in training) used 8-bit AdamW on AMD MI300X and NVIDIA A100/H100. For prod inference, most ops teams use a single A100 or smaller GPU; quantization (int8/int4) reduces footprint by 2–4×. Batch size 1–4 typical for interactive ops queries.
Integration
Runs on HuggingFace transformers with PyTorch; BF16 + Flash Attention 2 optimized. Pair with a telecom document retriever (e.g., dense embedding model from OTel-Embedding collection, or your own fine-tuned encoder) and reranker (OTel-Reranker). Max sequence length 1500 tokens: set context window and prompt template to fit. Standard LLM inference APIs (vLLM, TGI, llama.cpp) work; integrate to ops tools via REST/gRPC. Abstention support built in (model trained on 'abstention' flag): prompt engineering can route out-of-domain queries to fallback logic.
When it's not the right fit
- —General-purpose chat or unrestricted context-free QA: model is optimized for RAG with retrieved context; without context, it may hallucinate or over-generalize.
- —Multilingual operations: English-only; non-English telecom queries will underperform.
- —Real-time network automation without human review: model output should be verified before feeding into live network commands; not suitable as a direct closed-loop automation engine.
- —Low-latency, high-throughput customer-facing APIs: 8B model + RAG latency suitable for internal ops workflows; not optimized for sub-100ms SLAs or very large concurrent inference loads without heavy engineering.
Alternatives to consider
Mistral 8B or Llama 2/3 8B (fine-tuned in-house)
General-purpose models; no telecom domain training. Require significant curation and fine-tuning effort to match OTel's +7.2pp correctness lift. Useful if you want a blank slate and have large proprietary telecom datasets.
OTel-LLM (other family members: 70B, 1B variants)
Same lineage/training; pick by inference cost/accuracy tradeoff. 70B is more capable but ~2.7× VRAM cost; 1B is edge-deployable but lower quality.
Proprietary telecom APIs (e.g., Cisco AI Network Analytics, cloud LLM fine-tuning on AWS/Azure)
Outsourced domain training and support; data leaves your environment. Lower upfront engineering cost but higher operational dependency and data residency risk.
Related open models
FAQ
Can we run this entirely on-prem, with no external API calls?
Yes. OTel-LLM-8B-A1B-IT and the huggingface transformers library are open-source; download the model weights, host on your own hardware (GPU-equipped server or cluster), and expose via a local inference API. You control all data and compute.
What license governs commercial use?
Apache 2.0, which is permissive: allows commercial use, modification, and distribution under the same license terms. You may build products on top of it. Check your upstream base-model (LiquidAI/LFM2.5-8B-A1B) license as well; both must be compatible.
How do we fine-tune it on our proprietary telecom docs?
Use the same training recipe (AdamW, BF16, FSDP, Flash Attention 2); model card provides hyperparameters and framework (ScalarLM). Curate your dataset in the same format (prompt/completion/abstention fields) and run full-parameter or LoRA fine-tuning. Budget: similar GPU/compute cost to original OTel training on your dataset size.
Does this replace our telecom domain experts?
No. Model output should be reviewed before use in customer-facing, regulatory, or network-config contexts. It excels at accelerating knowledge lookup and triage; human domain expertise remains essential.
Build a Private Telecom AI Operating System
OTel-LLM-8B-A1B-IT is engineered for ops teams who need domain-specific knowledge agents running entirely on your infrastructure. Combine it with LLM.co's ops AI framework to automate support triage, compliance lookup, and network-planning workflows—with your data staying secure in-house. Let's design your telecom AI stack.