Six months ago, I started running local LLMs seriously on a Mac with 16 GB of unified memory. Most of my decisions were dictated by memory: which models fit, which quantization to use, and how much context I could afford.
Moving to 48 GB changed that.
Models such as Qwen3.8-27B became practical for everyday use, and my attention shifted toward a different problem: how well does the model work during a real coding-agent session?
My current setup uses LM Studio with Qwen3.8 and Splash, while OpenCode provides the agentic coding workflow.
With enough memory to run larger models comfortably, other constraints became much more visible.
Context length has a direct cost. Longer sessions accumulate tool output and code. Compaction can interrupt the flow earlier than expected. Thinking improves some tasks while adding latency to others.
A model that looks impressive in a short prompt can therefore feel quite different when it has to navigate a repository, inspect several files, use tools, make changes, and preserve enough context to continue reasoning about them.
That changed how I evaluate local models.
I care less about whether a model technically fits on my machine. I care more about how long I can work with it before the workflow starts getting in the way.
After quite a bit of experimentation, the stack has become relatively small:
Mac with 48 GB unified memory → LM Studio → Qwen3.8 / Splash → OpenCode
The interesting optimization work now happens around that stack: choosing a sensible context size, deciding when thinking is worth enabling, understanding why compaction happens, and balancing model capability against interactive speed.
There is still plenty to experiment with, but having more memory has made the trade-offs much clearer.
I documented my current configuration, what changed since the original 16 GB setup, and the lessons from using it for actual development work in the full article:
[https://ammar-najjar.com/blog/local-ai-setup-six-months-later/](https://ammar-najjar.com/blog/local-ai-setup-six-months-later/)
If you use local LLMs with coding agents, I would be interested in comparing experiences. Once model size and memory stopped being your main constraint, what became the next bottleneck?