Your AI. Trained on your data. Running on your servers.
Premforge deploys and fine-tunes open-weight language models inside your infrastructure. Sensitive data never leaves your perimeter, and the model you invest in stays yours.
For many teams, sending data to a third-party API is not an option.
Compliance
HIPAA, financial regulation, ITAR, client confidentiality. When contracts or law say data stays in-house, the model has to come to the data.
Predictable cost
Per-token pricing grows with every new user and workflow. Hardware you own turns AI into a fixed, plannable line item.
Control
No surprise model changes, deprecations, or rate limits. You decide what runs, when it updates, and who can access it.
From an empty rack to a model that knows your business.
Assess
Pick the use cases worth automating, audit your data, and size the hardware: what you have, what you need, what it costs.
Deploy
A production inference stack on your GPUs, with authentication, logging, and monitoring that fits your existing security model.
Fine-tune
Adapt the model to your documents, terminology, and tasks, then measure it against evaluations built from your real work.
Operate
Updates, retraining as your data changes, and a clean handover so your team can run it, or ongoing support if you prefer.
Six weeks from first call to a working model.
- Week 0 Discovery call We learn your constraints and goals, and tell you honestly whether on-prem makes sense.
- Weeks 1–2 Assessment & sizing Use case, data audit, model choice, hardware plan, and success metrics agreed up front.
- Weeks 2–4 Deployment The base model runs on your infrastructure, behind your access controls.
- Weeks 4–6 Fine-tuning & evaluation We train on your data and report results against the baseline you saw in week 2.
- After Handover or support Documentation and training for your team, or a support plan, your choice.
The 6-week pilot
One use case, one model, measured against your baseline. At the end you have a working system on your hardware and the numbers to decide whether to scale it.
- Use-case and data assessment
- Hardware sizing and model selection
- Deployment on your infrastructure
- Fine-tuning on your data
- Evaluation report and scale-up plan
Straight answers.
What hardware do we need?
It depends on model size and load. We size it during the assessment, and can work with GPUs you already own or help you specify a purchase.
Can it run fully air-gapped?
Yes. Once deployed, nothing needs to reach the internet.
How does it compare to the big cloud models?
Frontier cloud models are still stronger at broad, open-ended work. On narrow, well-defined tasks, a fine-tuned open model can be competitive. We measure that on your own tasks before you commit to scaling.
Who owns the fine-tuned model?
You do: the weights, the training pipeline, and the evaluation sets, subject to the base model's license.
Fine-tuning or RAG?
Often both. Retrieval keeps answers grounded in current documents; fine-tuning teaches the model your format, tone, and domain. We recommend the mix per use case.
Find out if on-prem AI fits your team.
A 30-minute call with an engineer, not a sales deck. If it isn't a fit, we'll say so.