Gemma 4 31B Hacks Onto 16GB Macs

This is a useful proof-of-possibility, not the default recommendation. It shows how far open-weight model squeezing has come, but it also highlights the hard ceiling of consumer laptop memory.

• –The hack depends on system-level memory tuning and aggressive quantization, so it is not a clean “just install and run” setup
• –Full GPU offload matters here; without it, the experience collapses back toward CPU-only territory
• –The post itself makes the practical tradeoff clear: 26B is still faster and more forgiving, even at higher precision
• –For local LLM hobbyists, this is valuable because it expands the testable envelope on Apple silicon
• –For everyday use, the cramped context window and low throughput make this more of an experiment than a serious production workflow