Running Frontier AI Locally Isn’t Free. It’s Just Different.

Gemma 4 shifts the cost curve - but only if you understand the memory math, benchmark traps, and deployment trade-offs. Intelligence is no longer a product you rent. It’s becoming a utility we own. Let’s start with the claim that should make any infrastructure engineer pause: a 31B-parameter model running in 1.5 GB of RAM. If your first instinct was to check the math, you’re right to doubt it. At 4-bit quantization, 31B weights alone require roughly 15-17 GB of memory. Add KV cache, activation buffers, and context overhead, and you’re looking at a consumer GPU, not a Raspberry Pi.