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// 63d agoINFRASTRUCTURE
Reddit Debates Mac Mini RAM Tiers
A Reddit user in r/LocalLLaMA is weighing which Mac mini to buy for running local models, specifically asking whether the M4 base configuration with 16GB unified memory is enough for something like Gemma 4, whether 24GB would unlock meaningfully better model options, and whether waiting for a future M5 base model would be a worthwhile upgrade. The thread is fundamentally about cost-effective hardware selection for local inference, with memory capacity and model size as the core tradeoff.
// ANALYSIS
Hot take: for local LLMs, RAM capacity usually buys more practical headroom than a small generation bump in chip performance.
- –24GB is the more meaningful upgrade if the goal is to run larger or less aggressively quantized models without constant compromise.
- –The M4 vs M5 base-model question is likely a smaller difference than 16GB vs 24GB for this use case.
- –If budget is tight, the 16GB M4 can still be useful for smaller quantized models, but it is easier to outgrow.
- –If the goal is “bang for buck” on local inference, prioritize unified memory first, then chip generation second.
// TAGS
mac-miniapplem4m5local-llmgemmaunified-memoryramhardware
DISCOVERED
63d ago
2026-04-08
PUBLISHED
63d ago
2026-04-08
RELEVANCE
8/ 10
AUTHOR
felixen21