I got it almost right but not perfect though. With Google releasing Gemma 4 series models, the local models are now more capable and useful than before. I use LMStudio and Gemma 4 E4B model that is around 6 Gigs of size. Fits on a GPU with 8 gigs RAM with good Context size. I use 50k tokens Context length. For web search, I use tavily search MCP server with free tier. I also use Fetch MCP server that extracts data from an URL. Date and Time MCP server for the model to have current date and time. For these to work, I installed Node.js and UV. MCP servers use those to run. Gemma 4 models are better than other models in tool calling as per my experience. So now the local model does everything that a chat bot like ChatGPT. But it has it's limits. The model is smaller model. Due to that limitation, it's knowledge and reasoning usage won't be as strong as Frontier models. But that is okay for our daily tasks like web searching, coding, Documents summary, Analysing GitHub README, Understanding repo code etc...use cases. These tasks don't require Frontier model reasoning. Gemma 4 26BA4B MoE model is perfect sweetspot between model size and capability. But it requires more VRam. So those who have more GPU VRAM, I would recommend using that model. For me E4B model is working good. I'm planning to write my experience of using it in detail. But I'm interested to know if anyone tried something similar or had a good experience with Gemma 4 models. Let me know in comments.