# Private LLM Infrastructure: get your own model serving traffic, privately

An on premise LLM is a model running on hardware you control, inside your own network, instead of calling a vendor API. Most teams who want one already have the hard parts started: a GPU box, a chosen model, maybe a serving container. What stalls them is the leg in between, getting that model exposed as a reliable private API, reaching it securely from their application, and trusting what comes back. That is the work Axionry does: vLLM serving tuned to your GPU, a private network path with no public exposure, schema-validated responses, and one real end-to-end request through your actual product. It starts with a fixed-fee assessment, credited in full against the build.

## What does a private LLM deployment actually involve?

The path is always the same shape: your application, a controlled backend gateway, a private encrypted transport, the self-hosted model service, a validated response, and the result rendered back in your product. Each hop is a place the project stalls. Teams usually have the model running locally and the application built, and are stuck joining the two in a way that is secure, survives a reboot, and returns well-formed data every time.

Concretely, the work is: expose the model as a private OpenAI-compatible API through vLLM so your backend talks to it like any hosted provider; put the GPU host and your backend on a private network so the model endpoint is never publicly reachable; tune the serving layer for your specific hardware; constrain the output so responses are well-formed by construction; and prove it with one real request through the real application.

## How do you tune an LLM serving layer for on-premise hardware?

Quantisation matched to the GPU's native kernel path, KV cache configuration sized to your real context lengths, continuous batching so concurrent requests share the GPU properly, and prefix caching where prompts share a common head. Together these move tokens-per-second under concurrency far more than swapping models does.

Then it gets measured, not assumed: tokens per second, time to first token, and how many concurrent requests the box actually holds before latency degrades. You get those numbers as a documented baseline, so later changes can be compared against something real.

## How do you keep a self hosted LLM private and secure?

The model port binds to a private interface only and is never exposed to the internet. A private overlay network joins the GPU host to your application infrastructure, verified as a direct peer connection rather than a relayed one, with access rules limiting which machine may reach the model port and which named people may reach SSH. Secrets live in a managed secret store and are read at runtime, never in code or environment files.

The service layer holds no write credentials to your database or storage, so unauthorised writes are structurally impossible rather than merely blocked. Output can be review-only, with no automatic save or apply, which is usually what regulated buyers need in order to approve a first deployment at all.

## How do you stop a private model returning wrong answers?

With a deterministic gate between the model's answer and your application: code, not another model. A fixed list of rules, each returning pass or fail, giving the same verdict for the same input every time. Totals in the response reconcile against the source rows. Every number cited is traceable to the input. Dates fall inside the selected period. Categories come from your allowed list. Required fields are present, reasoning text and prohibited content are absent.

A failed rule is never silently corrected. The service retries once with the failure fed back to the model, and if it fails again the response is returned as rejected with the failing rules listed, so your interface can show a needs-review state instead of a confident wrong answer. A second model critiquing the output is a useful layer on top of this, never a substitute for it.

## When does self hosting an LLM actually make sense?

Three situations. Data that legally or contractually cannot leave your network, which is most healthcare, tax, legal, defence and public-sector work. Volume, where consistent five-figure monthly inference spend starts to beat per-token pricing. And control, where you need a model version pinned for years or latency that does not depend on someone else's queue.

Outside those three, an API is usually the right answer and we will say so. A private deployment you did not need is an expensive way to acquire operational burden.

## What hardware do you need to run an LLM on premise?

It depends on model size, concurrency and latency targets, and getting it wrong is the most expensive error in the project. A quantised mid-size model serving a handful of internal users runs on a single workstation-class GPU. A desktop AI supercomputer such as NVIDIA's DGX Spark covers small-team inference and fine-tuning without a datacentre. Serving a large model to hundreds of concurrent users means multi-GPU nodes, fast interconnect, and real power and thermal planning.

If you have already bought hardware, we work with what you have. If you have not, sizing comes out of the assessment, against measured throughput rather than a vendor spec sheet.

## Pricing

| Stage | What happens | Timeline |
| --- | --- | --- |
| Assessment | Your stack examined end to end, existing work classified as preserved or replaced with reasons, connection design, implementation plan, and acceptance criteria written as testable statements | Five business days from access |
| Implementation | Delivered as separately accepted checkpoints, each with criteria agreed before work starts and invoiced only after you accept it | About three weeks |
| Handover | One real request through your real application, runbook, recorded handoff, all access removed with written confirmation | Included |

Fixed-fee assessment, credited in full against the build. No upfront payment and no escrow required: you hold every dollar until a checkpoint is delivered and accepted, and thirty days of defect correction is included. Scope and price are set on a call once we know your hardware, model and concurrency.

## FAQ

### What is the difference between an on premise LLM and a private LLM?

On premise means the hardware is physically yours, in your building or your rack. Private LLM is broader: it also covers dedicated GPU capacity inside your own cloud account, isolated to you but rented. Both keep your data out of a shared vendor API, and the serving and validation work is nearly identical.

### Can you work with NVIDIA DGX Spark and vLLM?

Yes. DGX Spark, single-GPU workstations, multi-GPU nodes and dedicated cloud GPU instances, served through vLLM as a private OpenAI-compatible API. If your model and container are already running, that work is preserved rather than rebuilt.

### Do you have experience with regulated data?

Yes. A recent engagement was for a healthcare client processing patient medical reports who needed the model to stay entirely inside their own infrastructure. Same architecture as most private deployments: application, controlled backend gateway, private encrypted transport, self-hosted model service, schema-validated response.

### Is a self hosted LLM cheaper than the OpenAI or Anthropic API?

Not at low volume. Below roughly five figures a month of inference spend an API almost always wins once engineering and operations are counted. Above it, owned or reserved capacity starts to win. The assessment builds that comparison against your real token volumes before you commit to either.

### Can the deployment be fully air gapped?

Yes. Fully offline deployments with no outbound network access are supported, including model and dependency mirroring, offline updates and local evaluation. This is typically what defence, healthcare and public-sector engagements require.

### Who owns the accounts and the code?

You do, throughout. Cloud accounts are created and held by you, access is named and limited-privilege, code and configuration are delivered continuously into your repository, and all our access is removed at handover with written confirmation. You also get a runbook covering start, stop, model upgrade, key rotation, metrics and recovery.

### How do you handle a first engagement with a new client?

A fixed-fee assessment first, credited in full against the build. Then implementation in separately accepted checkpoints, with acceptance criteria written as testable statements before work starts, each invoiced only after you accept it. There is no upfront payment and no escrow is required. You are welcome to appoint an independent technical reviewer to evaluate deliverables against those criteria, and thirty days of defect correction is included.

### How much does an on premise LLM deployment cost?

It depends on your hardware, model and concurrency, so the price is set on a call once we know those three. The structure is fixed: a fixed-fee assessment credited in full against the build, then implementation in checkpoints, each invoiced only after you accept it. There is no upfront payment, and thirty days of defect correction is included.

### How long does it take to deploy a private LLM?

The assessment takes five business days from the moment we have access, and covers your stack end to end, the connection design and testable acceptance criteria. Implementation runs about three weeks as separately accepted checkpoints. Handover, including one real request through your real application and a runbook, is part of the same engagement and adds no extra stage.

### What GPU do I need to run an LLM on premise?

It depends on model size, concurrency and latency targets. A quantised mid-size model for a handful of internal users runs on a single workstation-class GPU, while a large model serving hundreds of concurrent users needs multi-GPU nodes. If you have not bought hardware yet, we size it during the assessment against measured throughput, so you buy what your workload needs.

### How does our application connect to a self hosted model?

Through a private OpenAI-compatible API served by vLLM, so your backend talks to it the way it would talk to a hosted provider. The GPU host and your backend sit on a private overlay network, the model port is never exposed to the internet, access rules limit which machine can reach it, and secrets are read at runtime from a managed secret store.

### What happens when a private model returns a wrong or malformed answer?

A deterministic gate written in code checks every response before your application uses it: totals reconcile, numbers trace to the input, dates and categories are valid, required fields are present. A failed rule triggers one retry with the failure fed back. If it fails again, the response comes back rejected with the failing rules listed, so your interface shows a needs-review state.

### When should we not self host an LLM?

When none of the three reasons applies: data that cannot leave your network, inference spend high enough to beat per-token pricing, or a need to pin a model version or control latency. Outside those, a hosted API is usually the better answer and we will tell you so. A private deployment you did not need mostly adds operational burden.

### Our model already runs locally. Can you finish the deployment?

Yes, that is the most common starting point. Teams often have the GPU box, the model and a serving container working, then stall on exposing it securely and getting reliable output. The assessment classifies your existing work as preserved or replaced, with reasons, so finished work is kept and only the missing legs get built.

### What do we receive at handover?

A working path proven by one real request through your actual application, a documented performance baseline covering tokens per second, time to first token and concurrent capacity, a runbook, and a recorded handoff session. All of our access is removed with written confirmation, and thirty days of defect correction is included after that.

*Last updated: 2026-09-20*
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Axionry is an AI product development and cloud cost optimization studio founded by Neeraj Sharma. Contact: neeraj@axionry.com. Book a call: https://axionry.com/book