Inferenza locale: è davvero la risposta ai rincari dell'AI?

In recent months, the costs of AI tools have been rising. Between GitHub Copilot, ChatGPT, Claude, and new pricing models, many developers are starting to wonder whether local inference can become an economically viable alternative. In this video, I try to answer the question with a very practical approach: How much does it cost to run AI models locally today? What hardware do you really need? Does it make sense to buy a Mac with 128 GB of RAM or a dedicated GPU? Can DeepSeek and Qwen replace ChatGPT or Claude? What impact do power consumption, performance, and response quality have? Privacy, data sovereignty, and dependence on American providers Do OpenAI and Anthropic's price increases really change the picture? My conclusion is that local inference is now a solved problem from a technical standpoint, but not yet from an economic one. However, it could become an important element in containing costs, reducing dependence on providers, and ensuring greater control over your data. If you work in software engineering, AI engineering, or are considering a local setup for coding agents and AI assistants, this video might help you get a better idea of ​​the current situation. #AI #LocalInference #DeepSeek #Qwen #ChatGPT #Claude #OpenAI #Anthropic #SoftwareEngineering #AIEngineering #CodingAgent #LLM #MachineLearning #MacBookPro #NVIDIA #Productivity #DeveloperTools #BitAligners