GoKawiilGuide details Mac setup for local LLMs with Ollama, OpenCode and Docker sbx sandboxes
A developer outlines a workflow for running local large language models on an Apple MacBook Pro M5 with 48GB RAM, combining Ollama for model management, OpenCode as the development harness, and Docker's sbx sandbox tool for isolation. The setup uses models like Qwen 3.8 27B and Gemma 4 31B, pulled via Ollama and configured through a custom sbx kit for each project.
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This reflects a growing trend of developers moving away from cloud-based frontier models toward capable local alternatives that run entirely on consumer hardware. Sandboxing local models via Docker also addresses safety concerns, since unsupervised local LLMs can execute unintended actions on a user's machine, much like frontier models require sandboxing at scale.
- Local LLMs like Qwen 3.8 and Gemma 4 are now viable for real web development work on high-memory Apple Silicon Macs.
- The stack combines Ollama for model serving, OpenCode as the coding harness, and Docker's sbx tool for sandboxing.
- A 48GB MacBook Pro M5 is described as a practical sweet spot for running 30B-parameter models with solid context length.
Apple MacBook Pro (M4, 48GB) — Running 30B local models with a usable context window takes serious unified memory, which is exactly why the article calls a high-RAM Apple silicon MacBook Pro the sweet spot for local LLM work. Ollama's MLX support means these chips chew through inference fast, so you can run Ollama, OpenCode and Docker sandboxes all at once without leaving your desk.
See Apple MacBook Pro (M4, 48GB) on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: tensorsandtokens.com, 2026-09-11
Published there as: “Setting up OpenCode with Ollama and sbx on Mac”
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