Offline & On-Premise LLM Development
An offline LLM is a large language model deployed entirely inside your own infrastructure — on-premise servers, a private cloud tenancy or a fully air-gapped network — so no prompt, document or customer record ever leaves your perimeter. Webify.AI handles model selection, fine-tuning, private RAG, GPU sizing and compliance evidence, typically going live in six to ten weeks.
0
data leaving your network
6-10 wks
assessment to production deployment
60-80%
lower run cost at sustained volume
The problem today
- Legal or regulatory rules that forbid sending data to a third-party AI API
- Unpredictable per-token costs that scale badly with heavy internal usage
- Public models that know nothing about your products, policies or terminology
- Air-gapped environments where no external service can be reached at all
Outcome
Regulated organisations run generative AI on their own hardware, with zero data leaving the network and no per-token vendor bill.
Private, on-premise and air-gapped LLM deployments with full data sovereignty.
How we deliver it
Assess
Use cases, data classification, compliance obligations and hardware budget mapped to a target model size.
Select & tune
Open-weight models such as Llama, Mistral or Falcon evaluated, then fine-tuned on your domain corpus.
Ground
Private RAG over your document stores with permission-aware retrieval so answers cite internal sources.
Deploy & govern
GPU provisioning, inference serving, quantisation, monitoring, red-teaming and audit logging.
What's included
On-premise & air-gapped
Full installation inside your data centre or isolated network with offline model and dependency mirrors.
Domain fine-tuning
LoRA and full fine-tunes on your tickets, contracts, manuals and transcripts for domain-accurate output.
Private RAG
Vector search over SharePoint, Confluence, file shares and databases with row-level access control.
GPU optimisation
Quantisation, batching and serving configuration to maximise throughput per GPU you already own.
What you receive
- Model evaluation report and selection rationale
- Fine-tuned model weights held in your environment
- Private RAG pipeline and ingestion jobs
- GPU infrastructure and inference serving stack
- Compliance documentation and audit logging
Frequently asked questions
What is an offline LLM?
A large language model that runs entirely on infrastructure you control, with no external API calls. Prompts, documents and outputs stay inside your network, which makes it suitable for regulated and air-gapped environments.
Which models can be deployed on-premise?
Open-weight families including Llama, Mistral, Falcon, Qwen and Gemma, chosen against your accuracy targets, language needs and available GPU memory.
What hardware is required?
A single 48GB GPU handles many internal assistants at moderate concurrency. Larger models or high concurrency need multi-GPU nodes; we size the cluster during assessment before you buy anything.
Does an offline LLM help with HIPAA, GDPR or SOC 2?
Yes. Keeping data inside your controlled environment removes third-party processing from scope, and we supply architecture diagrams, access controls and audit logs as compliance evidence.
Can it be updated without internet access?
Yes. Model and dependency updates are delivered through a signed offline mirror process approved by your security team.
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