Local LLMs Now Viable for Coding and Summarization Tasks via Ollama

Recent advances in local large language models have made them significantly more capable for tasks like code generation and document summarization, reducing reliance on cloud-based AI services. Models such as Google's Gemma 4 and Alibaba's Qwen are now recommended for coding tasks, with Gemma 4 12B requiring around 6.7 GB of VRAM in its quantized form. Ollama has emerged as a widely used platform for deploying these models on Windows, Mac, and Linux, integrating with tools like Visual Studio Code and JetBrains AI Assistant. To get the best results, users are advised to carefully select a model suited to their hardware and intended task, and to fine-tune runtime parameters accordingly. While cloud-based models from providers like Anthropic and OpenAI still hold an edge in overall capability, local alternatives have become practical enough to merit serious consideration for personal and developer workflows.