Private & On-Prem LLM Deployment
We stand up production-grade language models inside your perimeter: on your hardware, in your VPC, or in an air-gapped room. Your prompts, documents and outputs never touch a third-party API.
Why teams bring us in
Public AI APIs are fast to try and hard to approve. Legal wants data residency, security wants an audit trail, and finance wants a bill that doesn't grow with every token. A private deployment answers all three, as long as it is built to production standards from day one.
We start with the question that decides everything else: what will people actually ask the model to do? Drafting, summarization, extraction and search each favour different model sizes, context lengths and hardware. We benchmark three to five candidate models on a set of your real tasks, then size the hardware to hit your latency target at expected peak load, with headroom for growth.
- Model selection: benchmarked against your own tasks, not leaderboard scores
- Serving stack: vLLM or TensorRT-LLM behind an OpenAI-compatible API your teams already know
- Retrieval (RAG): connected to SharePoint, Confluence, S3 or your DMS with permission-aware search
- Access control: SSO, per-team quotas, prompt and response logging to your SIEM
- Hardening: network isolation, secrets management, signed model artifacts
Teams that get the most from this service
Security-first enterprises
Your security review has blocked public AI APIs, but the business still wants AI assistants and automation.
Teams with sensitive IP
Source code, research, pricing models or M&A documents that should never be sent to a third party.
Organizations with idle GPUs
You already own hardware from an earlier ML push and want to put it to productive use.
Four phases, each with a sign-off.
The production build is designed to look like any other internal service your platform team already runs. Models sit behind an OpenAI-compatible gateway, so existing applications and SDKs work unchanged. Every request is authenticated through your identity provider, rate-limited per team, and logged to your SIEM with the prompt, response, model version and latency. Nothing about the system depends on Imgori once it is handed over.
| Phase | What happens | You receive |
|---|---|---|
| Discovery | Workloads, data sensitivity, hardware inventory, latency targets | Architecture decision record |
| Pilot | One model, one use case, real users behind SSO | Working endpoint + eval report |
| Production | HA serving, autoscaling, monitoring, runbooks | Signed-off production cluster |
| Handover | Training for your platform team, on-call playbooks | Docs + recorded sessions |
What you keep when we're done.
Private inference endpoint
OpenAI-compatible API running on your infrastructure, with keys and quotas per team.
Evaluation harness
A test suite built from your real questions so you can compare models and catch regressions.
Operations runbook
Scaling, patching, model upgrades and incident steps your team can follow without us.
Tools we use
Chosen per client. We work with what your team already runs wherever it makes sense.
Questions about Private & On-Prem LLM Deployment
How long does a first deployment take?
A pilot with one model and one use case is usually live in three to five weeks from kickoff. A production build with high availability, monitoring and security controls typically takes another four to eight weeks, depending on your change-management process.
What hardware do we need?
It depends on the model and traffic. A 7–8B parameter model can serve a department on a single L4 or L40S. A 70B model for company-wide use typically needs two to four H100s or equivalent. The assessment gives you exact sizing and a bill of materials.
Can it work fully offline?
Yes. We package model weights, containers and dependencies for air-gapped installation, verify checksums on arrival, and document an offline update process.
Will quality match hosted models?
For most business tasks, current open-weight models perform on par with leading hosted models, especially when combined with retrieval over your documents. We measure this on your tasks before you commit, and we tell you plainly if a task is not a good fit.
Often paired with
GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Explore service →SVC-03Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Explore service →SVC-04MLOps Pipeline Setup
Automate training, evaluation, deployment and monitoring so models ship like software.
Explore service →Talk to an engineer about Private & On-Prem LLM Deployment.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.