Developer finds context length and latency matter more than RAM in local AI setup

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A developer running local large language models on a Mac upgraded from 16 GB to 48 GB of unified memory, finding that model size was no longer the primary constraint. With larger models like Qwen3-27B now practical for daily use, attention shifted to how well models perform during extended coding-agent sessions. Key challenges that emerged include context length costs, compaction interrupting workflows, and the trade-off between reasoning quality and response latency. The developer settled on a streamlined stack using LM Studio, Qwen3 or Splash, and OpenCode for agentic coding tasks. The experience led to a shift in evaluation criteria — from whether a model fits in memory to how long it can sustain a productive development workflow.

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