"With each new generation of large language models, we're seeing smaller and more efficient LLMs for many use cases-small enough that you can run them on your own hardware. Most recently, we've seen a slew of new models designed for tasks like code analysis and code generation. The recently released Qwen3.5 model set is one example."
"To try out Qwen3.5 for development, I used my desktop system, an AMD Ryzen 5 3600 6-core processor running at 3.6 Ghz, with 32GB of RAM and an RTX 5060 GPU with 8GB of VRAM. I've run inference work on this system before using both LM Studio and ComfyUI, so I knew it was no slouch."
"Running the models on LM Studio did not automatically allow me to use them in an IDE. The blocker here was not LM Studio but VS Code, which doesn't work o"
Recent advances in large language models have produced smaller, more efficient versions suitable for running on consumer hardware without cloud services or token costs. Qwen3.5 represents a new generation of models designed for code analysis and generation tasks. Testing these models locally requires appropriate hardware—such as a mid-range desktop with adequate RAM and GPU memory—and hosting software like LM Studio. However, integrating locally-hosted models with development environments like Visual Studio Code presents technical obstacles. While the capability to run capable LLMs locally exists, practical implementation for everyday development workflows still faces integration challenges that prevent seamless adoption.
Read at InfoWorld
Unable to calculate read time
Collection
[
|
...
]