Self-Hosted AI Setup (Ollama, vLLM, LM Studio)

Overview

What we deliver

We deploy and operate self-hosted AI stacks so you control data, latency, and cost without depending on third-party APIs.

Many teams need AI capabilities without sending data to external providers. We design and deploy self-hosted AI infrastructure using Ollama, vLLM, LM Studio, Text Generation Inference, and similar runtimes on your hardware or private cloud. Our work covers GPU sizing, model selection, quantization choices, batching configuration, API gateway setup, authentication, observability, and autoscaling. We pick stacks based on your throughput targets, latency budgets, and team skill set. Whether you need a single-node developer setup or a multi-GPU cluster serving thousands of requests, we plan capacity, run load tests, and document operations. We also build the connective tissue: OpenAI-compatible endpoints, request routing, prompt caching, and logging. The result is a reliable private AI platform your team can build on, with predictable costs and full data sovereignty. We hand off runbooks so your operations team can keep it healthy.

Fit Check

Built for teams like yours

Who it's for

Regulated enterprises
Privacy-conscious startups
Government contractors
Research labs
High-volume AI products

Pain points we solve

Data leaving your network
Unpredictable API costs at scale
Vendor lock-in concerns
Latency from external APIs
Compliance and audit requirements

What's included

Capabilities

Everything we cover in this engagement.

GPU capacity planning
Ollama and LM Studio setup
vLLM and TGI production deployment
Model quantization (GGUF, AWQ, GPTQ)
OpenAI-compatible API gateway
Authentication and rate limiting
Observability and logging
Autoscaling and load balancing

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Requirements

We map workloads, throughput, and compliance needs.

02

Architecture

We design the stack, hardware, and networking layout.

03

Deployment

We install runtimes, models, and the API gateway.

04

Load testing

We validate throughput, latency, and failure modes.

05

Handoff

We document operations and train your team.

What you get

Deliverables & outcomes

What you get

Production inference cluster
OpenAI-compatible API endpoint
Model registry and version control
Monitoring dashboards
Operations runbook
Load test results and capacity plan

Outcomes you can expect

Full data sovereignty
Predictable per-token costs
Lower latency for in-region users
Reduced external API spend
Compliance-ready AI infrastructure

Timeline

3 to 6 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Ollama, vLLM, LM Studio, Text Generation Inference, Kubernetes

KPIs we track

Tokens per second, p95 latency, GPU utilization, uptime, cost per million tokens

Client stories

What clients say

"

We were drowning in tier-one tickets about password resets and appointment changes. They built a deflection layer on top of our help desk and kept their agents in the loop for anything sensitive. Volume to humans dropped 58 percent in two months and our patient NPS held steady. The hybrid handoff is the part most vendors get wrong. They did not.

P.M.

"

Two weeks before our seed round we still did not have a defensible model. Their fractional CFO rebuilt our three-statement forecast, pressure-tested the assumptions, and walked me through every line before the partner meeting. We closed 1.4M on the terms we wanted. The investor specifically called out how clean the financials looked compared to the last five decks she had seen.

Hannah B.

Proof