The Real Cost of Gemma 4: Why VRAM and Latency Trump Benchmarks

While AI articles typically highlight model capabilities, production deployment reveals a different story where VRAM footprint, latency requirements, and deployment complexity become the true selection criteria. Gemma 4's four distinct variants—spanning from edge-class to multi-GPU systems—demand constraint-driven selection rather than benchmark-driven choices. Developers who rely solely on performance scores often find their selected model incompatible with their hardware or latency budgets, making intentional alignment with real-world constraints the key to successful deployment.