AI infrastructure guides
In-depth, vendor-neutral reference guides for choosing, building and running LLMs locally and on-premise. Written for engineers, architects and decision-makers.
📄 Free: the one-page Local LLM Hardware Cheat-Sheet
Get it →
Hardware & cost
🎮
Best GPUs for local LLMs
VRAM tiers, value picks and what each card runs.
📐
How much VRAM for Llama 70B
The sizing formula, quantization and KV-cache.
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Cost of running LLMs locally
Full TCO, worked example and the break-even.
⚡
RunPod vs Vast.ai
GPU cloud compared: price, reliability, production.
🗜️
LLM quantization explained
GGUF vs GPTQ vs AWQ; quality vs size.
Deploy & methods
🛠️
Local LLM software stack
Ollama vs LM Studio vs vLLM: prototype to production.
🧩
RAG vs fine-tuning
Knowledge vs behavior — which to use, and when to combine.
🏢
Private ChatGPT for your company
Architecture, model, RAG, hardware and security.
On-premise & compliance
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On-premise vs cloud AI
Cost, control, compliance and the hybrid model.
🛡️
EU AI Act & on-premise
Risk tiers, GPAI rules and a compliance checklist.
For the full picture on enterprise on-prem AI:
💾 On-premise LLM observatory →