Local Model Training & Private AI
Fine-tune open-weight language models on your data and run them on your own servers or private cloud, so sensitive data never leaves your control.
Some data shouldn’t leave the building: client files, health or financial records, proprietary designs, source code. For those cases we train and run models locally, on your servers or a private cloud you control, with no third-party API involved.
We choose an open-weight model that fits the task and your hardware, prepare and clean your training data, fine-tune with efficient methods such as LoRA, and measure the result against a held-out test set before deploying it behind an internal API.
What’s included
Hardware sizing
Recommending GPUs, memory and storage for the models and traffic you expect.
Model selection
Comparing open-weight models for quality, speed, licence terms and hardware fit.
Data preparation
Collecting, cleaning, de-identifying and formatting your training data.
Fine-tuning
Parameter-efficient training (LoRA and QLoRA) that adapts a model to your domain.
Evaluation
Benchmarks on your own tasks, compared against the base model and alternatives.
Private deployment
Serving models on your infrastructure with authentication, logging and monitoring.
Typical projects
- Private assistants for legal, medical or financial documents
- Code assistants trained on internal repositories
- Classifying and routing documents on-premises
- Domain-specific writing and summarization
- Air-gapped or offline AI deployments
Tools & technology
- PyTorch
- Hugging Face
- LoRA / QLoRA
- llama.cpp
- vLLM
- Ollama
- Linux GPU servers
Frequently asked questions
What hardware do we need?
It depends on model size and traffic. Small models run on a single workstation GPU; larger ones need data-centre GPUs or several cards. We size it with you before anything is bought.
Is a local model as good as the big cloud models?
Not at everything. On a narrow task, a fine-tuned open model can match or beat a general cloud model while costing less per request and keeping data private. We test on your tasks so you can decide with real numbers.
How much data do we need to fine-tune?
Often less than people expect. A few hundred to a few thousand good examples can noticeably improve a narrow task. Quality matters more than volume.
Often paired with
AI Integration
Add large language model features to your software: assistants that answer from your documents, smarter search, summaries and data extraction.
Learn moreDevOps, CI/CD & Cloud
Continuous integration and deployment pipelines, containers, infrastructure as code and reliable hosting, so every change is tested automatically and released safely.
Learn moreSecurity Upgrades & Hardening
Planned, tested patching, hardening and major upgrades for Linux and Windows servers, with minimal downtime and a written record of every change.
Learn moreReady to get started?
Tell us about your systems and goals, and an engineer will reply with next steps for Local Model Training & Private AI.