**Google Gemma 4 excels on local hardware** [developing]
Key Questions
What hardware requirements do Google Gemma 4 models have for local running?
Quantized Gemma 4 models from 2.5GB E2B to 26B A4B MoE fit on 8-18GB VRAM. They run offline on phones and laptops. Deployment is supported via tools like LM Studio.
How fast do Gemma 4 models perform on consumer devices?
These models achieve 5-13 tokens per second on phones and laptops. Benchmarks demonstrate elite coding and reasoning capabilities on consumer hardware. They match Claude Pro productivity without subscription costs.
Can Gemma 4 models replace paid AI services like Claude Pro?
Yes, Gemma 4 provides comparable coding and reasoning performance without productivity loss. Open multimodal models excel locally on everyday gear. Related picks highlight best local LLMs by hardware tier and use case.
Open multimodal models from E2B (2.5GB) to 26B A4B MoE fit 8-18GB VRAM quantized, run offline on phones/laptops at 5-13 t/s, replace Claude Pro without productivity loss. Benchmarks show elite coding/reasoning on consumer gear. Deployment buzz via LM Studio.