Encoder-Free AI explained: The architecture behind Google’s Gemma 4 12B

A vast majority of multi-modal AI systems function as a relay race. For example, an image will come in through the Vision ...

Context compression finally works in production: new research cuts LLM input 16x without the accuracy hit

LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by NYU and Columbia.

Google's new open source Gemma 4 12B analyzes audio, video — and runs entirely locally on a typical 16GB enterprise laptop

For enterprise leaders aiming to decentralize their AI workloads, Gemma 4 12B offers a rare combination of edge-friendly ...

Google's latest on-device AI model is custom-made for your laptop

Google has released the Gemma 4 12B multimodal agentic AI model that's designed to run on consumer laptops without dedicated ...
Semiconductor Engineering

NPU Acceleration For Multimodal LLMs

Transformer-based models have rapidly spread from text to speech, vision, and other modalities. This has created challenges for the development of Neural Processing Units (NPUs). NPUs must now ...

Google drops Gemma 4 12B: A game-changing AI that runs on your laptop

India, June 7 -- Google has announced the launch of Gemma 4 12B, a dense multimodal model featuring a unified, encoder-free ...