--- title: The Complete Guide to Running AI Locally: Everything You... description: A practical guide to running AI models locally on your own hardware. Covers the core tradeoffs between local and cloud AI, hardware requirements by memory... canonical: https://daily.dev/posts/the-complete-guide-to-running-ai-locally-everything-you-need-to-know-before-you-start-3weeeznf1 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Complete Guide to Running AI Locally: Everything You Need to Know Before You Start | daily.dev og:description: A practical guide to running AI models locally on your own hardware. Covers the core tradeoffs between local and cloud AI, hardware requirements by memory... og:url: https://daily.dev/posts/the-complete-guide-to-running-ai-locally-everything-you-need-to-know-before-you-start-3weeeznf1 og:image: https://api.daily.dev/og/posts/3WeEEznF1.png og:image:alt: The Complete Guide to Running AI Locally: Everything You Need to Know Before You Start og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Complete Guide to Running AI Locally: Everything You Need to Know Before You Start **[Medium](https://daily.dev/sources/medium_js)** · 11 min read · 0 upvotes · 0 comments ## Summary A practical guide to running AI models locally on your own hardware. Covers the core tradeoffs between local and cloud AI, hardware requirements by memory tier, how to choose between runtimes (Ollama, LM Studio, llama.cpp, MLX, vLLM), and how to pick the right model type (coding, vision, reasoning, generalist). Explains quantization formats like Q4_K_M and Q8_0, how to connect a local model to a coding agent like OpenCode, and the key limitations: tool use inconsistency, vision pipeline complexity, hallucinations, and cloud models still outperforming local ones on complex tasks. Includes common mistakes and the author's own MacBook Pro M4 Max setup with qwen3-coder:30b. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How much RAM do I need to run a 30B parameter local LLM with Ollama? You generally need around 48 GB of unified memory or RAM to comfortably run strong 30B-class quantized models. As a rough guide, 8 GB supports 1B-4B models, 16 GB supports 7B-8B, 24 GB supports 12B-14B, 32 GB supports some 20B-30B models, and 64 GB or more handles larger models with heavier context windows. A 30B quantized model download itself can require around 20 GB of storage. _Developers sizing hardware for local inference can track practical setup guides like this on daily.dev._ ### Can Ollama run cloud-hosted models as well as local ones? Yes, Ollama supports both local and cloud-hosted models, with the model name distinguishing them: a tag like model-name:30b runs locally if installed, while a tag like model-name:large-cloud routes inference to Ollama's own cloud infrastructure. Local inference is unmetered and limited only by your hardware, while paid Ollama plans apply to cloud usage, larger hosted models, and concurrency features, not local speed or limits. _Anyone deciding between local and cloud inference costs can follow tool changes like this on daily.dev._ ### Why does a coding model like qwen3-coder fail to process a screenshot? A text-only coding model such as qwen3-coder:30b cannot process images because it lacks multimodal (vision) capability entirely; parameter size or coding strength does not grant vision support. To handle screenshots, diagrams, or visual documents, a separate vision-language model is required, and the client application and provider integration must also correctly pass the image data end-to-end. _Developers choosing between specialist and multimodal local models can compare trade-offs like this on daily.dev._ ## Similar posts on daily.dev - [Local AI Models for Coding 2026: Ollama Setup and Best Models](https://daily.dev/posts/local-ai-models-for-coding-2026-ollama-setup-and-best-models-sohghmj3p) · Alex CloudStar · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#local-ai](https://daily.dev/tags/local-ai), [#ollama](https://daily.dev/tags/ollama) [View this post on daily.dev](https://daily.dev/posts/the-complete-guide-to-running-ai-locally-everything-you-need-to-know-before-you-start-3weeeznf1) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Complete Guide to Running AI Locally: Everything You Need to Know Before You Start","url":"https://daily.dev/posts/the-complete-guide-to-running-ai-locally-everything-you-need-to-know-before-you-start-3weeeznf1","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-complete-guide-to-running-ai-locally-everything-you-need-to-know-before-you-start-3weeeznf1"},"datePublished":"2026-07-23T12:09:25.563Z","dateModified":"2026-09-14T09:05:23.468Z","description":"A practical guide to running AI models locally on your own hardware. 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