Google releases Gemma 4 12B for local inference

Summary

Developers can now deploy multi-modal reasoning and agentic workflows locally on standard laptops without cloud inference costs or token metering. Native audio eliminates separate encoding overhead, reducing latency and memory for time-sensitive applications.

Why it matters

Developers can now deploy multi-modal reasoning and agentic workflows locally on standard laptops without cloud inference costs or token metering. Native audio eliminates separate encoding overhead, reducing latency and memory for time-sensitive applications.

Sources

1. 1.Small enough to run locally on a mere 16GB of VRAM or unified memory
2. 2.performs nearly as well as Gemma 4 26B — but at less than half the total memory footprint
3. 3.passes those inputs directly into the LLM backbone
4. 4.project[s] the raw audio signal into the same dimensional space as text tokens
5. 5.Cloud is convenient, but you're paying per token forever, and your prompts go through someone else's server. local = one time setup, private, zero ongoing cost.