Self-Hosted Local LLM Deployment & Private On-Premise AI
Service Summary
I configure and deploy open-source large language models (Llama 3, Mistral, Qwen, DeepSeek) on private enterprise servers and workstations. Using Ollama, vLLM, and Docker Compose, I help businesses eliminate recurring per-token cloud API bills, meet strict data-residency standards, and retain 100% intellectual property ownership.
What You Receive
Hardware Benchmarking & Quantization
Selecting the optimal parameter size (7B, 14B, 32B, 70B) and GGUF quantization (Q4_K_M, Q8) for your specific VRAM and CPU limits.
Dockerized Ollama & vLLM Runtime
Production containerization with automated crash restarts, health checks, and GPU passthrough (NVIDIA CUDA / ROCm).
OpenAI-Compatible REST Proxy
Exposing standard /v1/chat/completions endpoints so your internal tools can switch from OpenAI to private AI instantly.
Modern Web UI & User Access Control
Deploying OpenWebUI or LibreChat with multi-user authentication, chat history, and model switching.
Transparent Fixed-Price Packages
Clear deliverables with no surprise hourly invoices. Includes free post-launch support.
Silver
Single server or workstation setup for small team experimentation.
Delivery: 2–3 Business Days
- Ollama Installation & Service Configuration
- 1–2 Optimized LLMs Downloaded & Benchmarked
- OpenAI-Compatible Local API Endpoint
- Hardware Resource Tuning
- Setup Guide & Model Switch Cheat Sheet
- 30 Days Breakdown Support
Gold
Production Docker stack with multi-user web UI and document chat.
Delivery: 4–6 Business Days
- Docker Compose Full Stack Deployment
- OpenWebUI / LibreChat Workspace
- GPU Acceleration & CUDA Driver Passthrough
- Private Document Chat (Local RAG)
- Nginx Reverse Proxy with SSL Encryption
- Basic Multi-User Role Administration
- 60 Days Warranty Support
Diamond
High-throughput enterprise AI cluster with automated failover.
Delivery: 7–10 Business Days
- High-Throughput vLLM Inference Engine
- Multi-GPU Parallelization (Tensor Parallel)
- Custom Model System Prompt Tuning
- Load Balancing & Queue Monitoring
- Air-Gapped / Intranet Security Configuration
- Live Team Training & Operator Handover
- 90 Days Priority Support
100% Offline Research LLM Workspace
Configured an air-gapped biological research workstation running Ollama and ChromaDB, allowing scientists to analyze proprietary findings without cloud exposure.
Outcome: Zero cloud API subscription costs and 100% on-premises data isolation.
Frequently Asked Questions
What hardware do I need to run a local LLM effectively?
For 7B or 8B parameter models (e.g. Llama 3 8B), a modern 8-core CPU with 16GB RAM is enough for basic speeds. For real-time conversational speeds (30+ tokens/sec), an NVIDIA GPU with 8GB–16GB VRAM (RTX 3060, 4060, 4070, or Mac M-series Apple Silicon) provides excellent performance.
Can our existing software easily switch from OpenAI to a local LLM?
Yes. Ollama and vLLM provide drop-in OpenAI-compatible API routes (/v1/chat/completions). In most cases, you simply point your existing apps to http://your-server-ip:11434 instead of https://api.openai.com.
Does local AI help meet GDPR data-residency requirements?
Yes. Because every prompt, query, and customer document is processed in memory on your own physical or cloud server, no data is shared with OpenAI, Microsoft, or third-party training pipelines.
Technologies & Protocols Used