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Infrastructure

On-premise AI servers and your own hardware

A model on your own hardware: how much VRAM it needs, which models run locally, what the setup costs, and when owning a server beats renting one.

InfrastructureJuly 21, 202613 min read

How to Build an On-Premise AI Server

A step-by-step guide to building a self-hosted AI server: picking the model, sizing GPU memory, the software stack, RAG, and security, with real configs and pricing.

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InfrastructureJuly 20, 202613 min read

On-Premise LLM vs Cloud AI: Real Cost Comparison

Open-source LLMs now handle most business tasks cloud AI does. The real 3-year cost, data-leak risk, and the point where running your own server starts winning.

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InfrastructureJuly 21, 20269 min read

How to Train an AI on Your Own Data: RAG Explained

You do not retrain an AI to know your company — you connect it to your documents with RAG, so answers come from real files, not guesses. How RAG works, step by step.

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InfrastructureJuly 21, 202611 min read

How Much VRAM Do You Need for a Local LLM?

Budget about 1 GB of VRAM per billion parameters in a quantized model, plus headroom for context and concurrent users. Sizing by model class and real GPU picks.

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InfrastructureJuly 21, 202610 min read

Best Open-Source LLMs to Self-Host in 2026

Qwen, Llama, DeepSeek, Gemma and Mistral now cover most business work and run on hardware you own. How the families differ, and how much VRAM each one needs.

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InfrastructureJuly 21, 20269 min read

LLM Quantization Explained: Q4 vs Q8

Quantization shrinks the weights of an LLM to fit your GPU VRAM. What Q4, Q6 and Q8 mean, how much quality you give up, and which one to run in production.

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InfrastructureJuly 23, 202610 min read

Fine-Tuning vs RAG: Which One Your Business Needs

RAG connects a model to your documents without retraining and updates the moment a file changes. Fine-tuning pays off for style and format, at a real cost.

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InfrastructureJuly 21, 20269 min read

Migrating From the ChatGPT API to Your Own Server

Most self-hosted LLM servers expose an OpenAI-compatible API, so many integrations only need a new endpoint and key. The migration steps, laid out without downtime.

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InfrastructureJuly 21, 20268 min read

Own AI Server or Rented GPU: Which Costs Less

Renting GPU time wins for pilots and bursty load; your own server pays for itself in 12-18 months of steady use, with full data control. A comparison in money.

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InfrastructureJuly 21, 20268 min read

On-Premise AI for Law Firms: Privilege Stays Intact

Cloud AI is effectively closed to law firms. A self-hosted model handles contract review, redline comparison and case search inside the firm, nothing leaving it.

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InfrastructureJuly 21, 20268 min read

On-Premise AI in Healthcare: Data Stays In-House

Medical data cannot leave the institution, ruling out cloud AI. A self-hosted model helps with documents, protocols and routine work; decisions stay with the doctor.

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InfrastructureJuly 21, 20268 min read

On-Premise AI for Banks: A Closed-Perimeter Setup

Banking secrecy rules out cloud AI. A closed, air-gapped model covers internal documents, staff support and analytics, with no prompt leaving the bank.

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InfrastructureJuly 23, 20269 min read

How to Run DeepSeek Locally on Your Own Server

Distilled DeepSeek runs on a single GPU, the full model needs far more VRAM. How to pick the version, what the setup needs, and how to connect it to your documents.

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InfrastructureJuly 23, 202610 min read

On-Premise AI for Manufacturing: Inside the Plant

A self-hosted model answers documentation and standards questions, assists operators, processes paperwork and trains staff, with drawings never leaving the plant.

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InfrastructureJuly 23, 202610 min read

Updating a Self-Hosted LLM Safely

Update for a reason, not on a schedule. Test a candidate model on real logged queries, keep the old one as a fallback, and roll back in minutes if quality drops.

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InfrastructureJuly 21, 20267 min read

Tower or Rack: Which AI Server Form Factor You Need

A tower suits 5-50 users and sits in the office; a rack unit is for loaded departments and 24/7 with redundancy and several GPUs. The model runs the same on both.

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