LocalLLaMA user weighs speed, stability, costs

Local LLM setups are getting to the point where the real win is operational simplicity, not bragging rights. This post is a good reminder that driver quality, runtime choice, and memory behavior can matter more than raw specs.

• –The progression from 96GB to 256GB/512GB Mac Studios and then an RTX Pro 6000 shows how fast local inference becomes a memory-capacity problem, not just a FLOPS problem
• –The 16GB MacBook Pro being more stable than a much larger machine is a strong signal that software stack maturity can outweigh hardware headroom
• –Trying Qwen, DeepSeek, Gemma, and MiniMax reflects the current local-LLM reality: users are chasing fit, latency, and reliability, not a single benchmark winner
• –The most useful answer here will probably be boring but practical: quantization, backend selection, context management, cooling, and driver stability
• –The broader signal is that local LLMs are moving from hobbyist tinkering into serious workstation planning, which is exactly where the hidden costs show up