Technology for emerging-market businesses and institutions.
Explore Deviceterra ↗Use local AI
with evidence.
Independent, practical guides for choosing hardware, models and deployment patterns without false certainty.
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What is local AI - and when should you use it?
A practical guide to running AI on your own computer, what you gain, what you give up, and where cloud AI still makes more sense.
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RAM, VRAM and model size explained simply
Why downloading a model is not the same as running it - and how to avoid recommendations that look precise but fail on your machine.
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Ollama vs LM Studio: which should you install?
Two popular ways to run models locally, compared by setup, usability, automation, privacy, and the kind of work you want to build.
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Qwen, Gemma, Llama or DeepSeek?
A family-level comparison for coding, reasoning, writing, vision, and general work - without pretending one model wins every job.
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How to keep company knowledge local
A practical architecture for private document assistants that retrieve useful evidence without sending an entire business archive to the cloud.
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Why “it runs” does not mean “it is reliable”
A downloaded model can answer a prompt and still fail the job. Here is how to test quality, speed, safety, and workflow fit before trusting it.
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How to run local AI without a dedicated GPU
A realistic CPU-only starting path for ordinary laptops, including model size, quantization, context and the tasks worth attempting first.
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How to benchmark a local AI model on your own computer
Measure time, memory, quality and reliability on the device that matters, then save an evidence card you can compare after every upgrade.
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How to secure local AI before using private data
A practical security checklist for model APIs, documents, logs, connected tools, users and backups before confidential work begins.
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Build a private document assistant with local AI
A step-by-step blueprint for turning approved PDFs, policies and manuals into a searchable local assistant with citations and permission boundaries.
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Local AI inference engines explained
Ollama, LM Studio, llama.cpp, MLX LM, vLLM, SGLang and mistral.rs solve different performance and deployment problems. Here is how to choose.
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Embedding models explained for local RAG
The hidden model behind document search: what embeddings do, how to choose one, why reranking matters and how to test retrieval before blaming the chatbot.
Read the complete guide →Start with your hardware.
Get a conservative model shortlist before downloading anything.