Private LLM development,
on your own hardware.
A private LLM runs where your data already lives. No prompt leaves the perimeter, no token is metered, and the model stays under your control from selection through to production.
What a private LLM actually is
A private LLM is a language model you host yourself, rather than one you rent through somebody else’s API. The weights sit on hardware you control, inference runs inside your network, and the request never crosses your boundary.
That distinction matters more than it sounds. With a hosted API, every prompt — and every document attached to it — is an outbound transfer to a third party. For a bank reconciling counterparty exposure, or a hospital summarising patient notes, that transfer is the whole problem. A private LLM removes it rather than papering over it with a contract clause.
What private LLM development involves
- Model selection
- Open-weight models chosen against the task, not the leaderboard. Most deployments end up running a mixture-of-experts configuration — several models behind one interface, each picked for what it is actually good at.
- Hardware sizing
- What the model needs in GPU memory, how much headroom concurrency demands, and whether that is one server or a cluster. Sized against your real load, not a benchmark.
- Serving and orchestration
- Models containerised with Docker and orchestrated on Kubernetes, so a deployment is reproducible and a rollback is a version change rather than a rebuild.
- Retrieval
- A private LLM on its own knows nothing about your business. Retrieval over your own documents and databases is what makes it useful.
- Governance
- Scoped permissions, session-limited access and an audit trail on every action, enforced in the system rather than in a policy document.
Private LLM or a public API?
A public API is the right answer for plenty of work. It is the wrong answer when the data is regulated, when the cost of inference scales with how much you use it, or when an outbound dependency is itself the risk.
The economics diverge sharply at volume. A hosted API bills per token, so a successful rollout raises the bill; a private LLM is a fixed infrastructure cost, and usage beyond that point carries no per-token charge. Enterprises automating a high-volume process usually find the crossover arrives sooner than expected.
How a private LLM deployment runs
Kickoff to production runs 10–12 weeks on a typical engagement. Discovery establishes the workload and the constraints; architecture fixes the model configuration, the retrieval design and the deployment target; build and integration follow; then the system runs in your environment, with your team.
Delivery is founder-led. The people who design the architecture are the people who implement it.
Frequently asked questions
What hardware does a private LLM need?
It depends on the model and the concurrency. A single quantised open-weight model serving a small team can run on one GPU server; a larger model serving a department concurrently needs more memory and usually a cluster. We size against your actual load during discovery rather than quoting a default.
Can a private LLM run with no internet access at all?
Yes. Fully air-gapped deployment is supported — the model, the embeddings, the data and the logs all stay inside, and no outbound network path is required for the system to function.
Do you fine-tune the model?
Sometimes, but retrieval usually solves the problem better and more cheaply. Fine-tuning changes what a model knows how to do; retrieval changes what it knows. Most enterprise requirements are the second kind.
Is private LLM development available in India?
Yes. Synthora AI Labs is based in Hyderabad and deploys into Indian enterprise infrastructure, including environments with RBI data localization and DPDP Act requirements.
Where this connects
See a private agent
run inside a perimeter.
A 30-minute walkthrough of an agent planning, retrieving, acting, and verifying — all on infrastructure that never touches the public cloud — followed by the questions that determine whether it fits your environment.
- Live, not slides
- Your workflow, if you bring one
- No unpaid proof of concept