Practical guides on local AI, server tuning and keeping your infrastructure yours. Written by the team that runs the machines.
From a fresh instance to your first streamed tokens: install Ollama, pull a quantized 70B model, expose a private API and connect your apps. Copy-paste commands included, no prior server experience required.
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The open reasoning model on a $129 card: quantization that fits in 24 GB, the sampling settings that matter, and honest tok/s numbers.
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One server, two containers, zero API keys. Wire n8n to a local Llama and put a free private model in the hot path of everything.
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We priced 30 days of moderate LLM usage against a flat GPU instance. The break-even point arrives earlier than you think.
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CLI-first versus GUI-first. We ran both on identical hardware and measured setup time, throughput and day-to-day ergonomics.
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ComfyUI on a Windows GPU instance, streamed to any laptop. Model folders, launch flags and the settings that matter for speed.
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SSH keys, firewall rules, fail2ban and automatic updates. The minimum viable hardening every fresh server should get on day one.
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Shared vGPU slices versus a full dedicated card. How passthrough works under KVM and what it changes for inference speed.
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