Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM - Ars Technica

kept by eddie jun 04

Google's Gemma 4 12B model uses Multi-Token Prediction and a streamlined multimodal encoder to run efficiently on laptops with 16GB RAM, matching larger models.

April 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac mini
A guide for running Gemma 4 models locally on a Mac Mini via Ollama, with commenters reporting that the 26B variant is too slow and memory-intensive for daily use, while smaller quantizations suffice for light tool-calling tasks.
also on Gemma 4, LM Studio, Google, #developer-tools
Gemma 4 WebGPU Kernels - a Hugging Face Space by webml-community
Gemma 4 E2B runs locally in-browser via WebGPU, letting users prompt the model directly without server-side inference.
also on Gemma 4, Hugging Face, #machine-learning, #generative-ai, #open-source, #ai
Running local models is good now
Local agentic coding models have reached surprising quality and usability over the past six months, now offering ~75% of frontier-model accuracy for many development tasks on a 64GB M2 Mac.
also on LM Studio, Google, #open-source, #developer-tools
How LLM Inference Works
LLM inference works by tokenizing input, computing embeddings through transformer layers, then generating tokens autoregressively with KV caching and quantization optimizations.
also on Hugging Face, #machine-learning, #generative-ai, #ai, #developer-tools
9 theses on AI | Sarthak Munshi
AI progress is constrained by long-task reliability, labor reallocation, cost inefficiencies of general APIs, the declining value of raw coding skills, inadequate benchmark testing, the limits of formal verification without strong specs, memory-bound local hardware advantages, the shift from data to environment-driven training, and the rising competitiveness of US open-weight models.
also on Hugging Face, #machine-learning, #open-source, #ai, #developer-tools