Private & Local LLM Deployment
Your data never leaves.
Air-gapped and on-premise LLM deployments for organizations where data sovereignty isn't optional.
We design and deploy production LLM infrastructure entirely within your environment — no data sent to third-party APIs, no model access through external services. Whether you are running on bare metal, in a private cloud, or a fully air-gapped network, we build inference stacks that give your team the capabilities of frontier models with the control posture of on-premise software.
What we build
Private LLM deployment is not a single product — it is an architecture decision that cascades through model selection, inference infrastructure, API design, and integration with the workflows that need to consume the model. We handle all of it.
Ollama Deployments
Ollama provides the fastest path to a private, API-compatible local inference endpoint. We configure Ollama deployments on your hardware or private cloud, select and pull the right open-weight models for your workload, and expose OpenAI-compatible endpoints your existing tools and agents can target without modification. We handle performance tuning, model versioning, and integration with your authentication layer.
vLLM Inference Servers
For production workloads requiring higher throughput, concurrent request handling, and enterprise reliability, we deploy vLLM inference servers. vLLM's PagedAttention architecture delivers significantly higher tokens-per-second than naive inference, making it the right choice when your deployment needs to serve multiple users or pipelines simultaneously. We configure continuous batching, GPU memory optimization, and horizontal scaling based on your expected load.
Open-Weight Model Selection
Choosing the right base model is as important as the infrastructure. We evaluate your task requirements, hardware constraints, and compliance posture against the current landscape of open-weight models — Llama 3, Mistral and Mixtral, Microsoft Phi, and Google Gemma — and recommend the right model for each use case. For organizations with diverse workloads, we often deploy multiple models behind a unified routing layer.
Fine-Tuning Pipelines
When a base model needs to be adapted to domain-specific language, formatting requirements, or task-specific behaviors, we build fine-tuning pipelines using LoRA and QLoRA to make that adaptation efficient and repeatable. We manage data preparation, training runs, evaluation against held-out benchmarks, and merging back to a deployable model artifact — the full pipeline from raw training data to a model your team can trust.
Quantization (GGUF / GPTQ)
Running large models on constrained hardware requires quantization. We select and apply the appropriate quantization scheme — GGUF for CPU and mixed CPU/GPU inference via llama.cpp, GPTQ for GPU inference with minimal accuracy degradation — based on your hardware and acceptable quality trade-offs. Quantization is not a one-size-fits-all decision; we test the output quality of quantized models against your actual tasks before committing.
API-Compatible Local Endpoints
We expose every local deployment behind OpenAI-compatible REST endpoints, which means your existing tools, agents, and integrations can point to your private infrastructure with minimal code changes. We add authentication, rate limiting, request logging, and usage tracking so the endpoint behaves like a managed service — with all the data control of on-premise infrastructure.
RAG Over Private Data
We combine your private LLM deployment with a private RAG pipeline — ingestion, chunking, embedding, and vector storage all running within your environment. Your documents never leave. We design retrieval pipelines over internal knowledge bases, document repositories, contract libraries, and any structured or unstructured data source your team needs the LLM to reason over accurately.
Who needs this
Private LLM deployment is the right architecture when the cost of a data breach, regulatory violation, or IP exposure exceeds the convenience of a managed API. These are the environments where we see that calculation most clearly.
Banks, investment managers, and fintech companies operating under regulatory frameworks that restrict data transmission to external services. Local LLM infrastructure keeps client data, transaction records, and proprietary models within the regulatory perimeter.
Clinical documentation, patient record analysis, and healthcare AI applications where PHI cannot be transmitted to third-party model providers. We design private LLM deployments that satisfy HIPAA technical safeguards from the infrastructure up.
Agencies operating classified or sensitive unclassified environments where cloud connectivity is restricted or prohibited. Air-gapped LLM deployments on government-controlled hardware with full auditability and no external dependencies.
Law firms and legal departments where client confidentiality obligations make sending documents and correspondence through external AI APIs legally and ethically untenable. Local LLM deployment keeps privileged information entirely under counsel control.
Organizations whose competitive moat is proprietary data, trade secrets, or internal processes that should never be exposed to third-party model training pipelines or inference logs.
How we approach private LLM deployment
Every private deployment starts with an honest assessment of your hardware, your use cases, and your compliance constraints — because the right stack for a CPU-only air-gapped server is different from the right stack for a GPU cluster in a private cloud.
Hardware and infrastructure assessment
We start by understanding what you are running on — GPU type and VRAM, CPU and RAM, network isolation requirements, and whether you need multi-node deployment. This dictates the inference stack, the quantization approach, and the model size ceiling. We do not recommend models or infrastructure that will not actually run reliably in your environment.
Model selection and evaluation
We benchmark candidate models against your actual tasks — not generic benchmarks — before finalizing the selection. A model that scores well on MMLU may be the wrong choice for your domain-specific extraction tasks. We run your real inputs through candidate models and evaluate output quality, latency, and context window behavior before committing.
Inference stack deployment and hardening
We deploy and configure the inference server, expose authenticated API endpoints, implement request logging and monitoring, and validate that the system performs correctly under concurrent load. For air-gapped environments we package all dependencies for offline installation.
Integration and handoff
We integrate the private LLM endpoint with the applications and workflows that need to consume it, validate that the end-to-end system performs correctly, and deliver full documentation including architecture diagrams, configuration references, and operational runbooks. Your team can maintain this without us.
Why AR Data
Private LLM deployment is fundamentally a systems engineering problem — not a machine learning problem. Getting a model to run locally is straightforward. Getting it to run reliably at production throughput, integrated with your existing infrastructure, within your compliance posture, with authentication and observability built in — that is the work. That is what we do.
Our background at Protocol Labs gave us deep experience with decentralized, self-hosted infrastructure systems where external dependencies are a liability by design. That same posture informs how we approach private LLM deployment: the system should work completely independently of any external service, including us.
We use agentic workflows in our own build process, which means we deliver in a fraction of the time a traditional shop would require — without cutting corners on the engineering. You get production-grade infrastructure, not a proof-of-concept that works in a demo environment.
Compliance and data sovereignty
The entire point of private LLM deployment is data control — and we design the compliance architecture before we write the first line of configuration. For healthcare clients, that means HIPAA-aligned infrastructure: encrypted storage, access logging, no PHI retention in inference logs, and documented data flows for audit purposes. For financial clients, it means deployment within the regulatory perimeter with audit trails that satisfy examiner requests.
We document the compliance posture of every deployment as part of the deliverable — not as a separate artifact you have to produce yourself when your compliance team asks questions. If your organization requires a security review or architecture review before deploying AI infrastructure, we can support that process with the documentation it needs.
Ready to keep your data on-premise?
30 minutes. We scope what you need — hardware, models, use cases, compliance requirements. No pitch deck.
