Gemma 4 E4B is small enough to run anywhere, but powerful enough to handle typical LLM workloads
Choosing the right locally-deployed large language model can be a bit of a hassle. Bulky LLMs, for example, can deliver accurate results, but you’ll need decent hardware to get them up and running. I use Mixture-of-Experts models for most of my LLM-heavy tasks, but even with their optimized nature, I can’t use MoE offloading to run 35B clankers on iGPU-laden systems with less than 8GB of memory.
Choosing the right locally-deployed large language model can be a bit of a hassle. Bulky LLMs, for example, can deliver accurate results, but you’ll need decent hardware to get them up and running. I use Mixture-of-Experts models for most of my LLM-heavy tasks, but even with their optimized nature, I can’t use MoE offloading to run 35B clankers on iGPU-laden systems with less than 8GB of memory.
Abeer Shenoy
India
India
Published by: aplhsindia.in
