News

The Download: Caiwei’s Three Things and Reasons to be Optimistic about...

AI Observer
News

Gurman: Siri upgrades ‘unlikely to be discussed much’ at WWDC next...

AI Observer
News

NVIDIA plans to establish a research center in Shanghai

AI Observer
News

The Download: chaos at OpenAI and the spa heated up by...

AI Observer
News

Ex-Siri head reportedly wanted Apple to choose Google’s Gemini over ChatGPT

AI Observer
News

10 people share how they use AI in their everyday lives

AI Observer
News

The impact of AI in your organization

AI Observer
AI Hardware

Huawei to ship 700000 Ascend AI chips in 2025 despite challenges...

AI Observer
News

SWE-Bench Performance Reaches 50.8% Without Tool Use: A Case for Monolithic...

AI Observer
News

How to Build a Powerful and Intelligent Question-Answering System by Using...

AI Observer
News

Build Custom AI Agents for Workflow Automation

AI Observer

Featured

AI Hardware

OpenBMB Releases MiniCPM4: Ultra-Efficient Language Models for Edge Devices with Sparse...

AI Observer
News

The concerted effort of maintaining application resilience

AI Observer
News

Ericsson and AWS bet on AI to create self-healing networks

AI Observer
Legal & Compliance

Meta buys stake in Scale AI, raising antitrust concerns

AI Observer
AI Observer

OpenBMB Releases MiniCPM4: Ultra-Efficient Language Models for Edge Devices with Sparse...

The Need for Efficient On-Device Language Models Large language models have become integral to AI systems, enabling tasks like multilingual translation, virtual assistance, and automated reasoning through transformer-based architectures. While highly capable, these models are typically large, requiring powerful cloud infrastructure for training and inference. This reliance leads to latency,...