Evolving challenges and strategies in AI/ML model deployment and hardware optimization have a big impact on NPU architectures ...

Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design” was published by researchers at ...

Moonshot AI’s Kimi K2.5 Reddit AMA revealed why the powerful open-weight model is hard to run, plus new details on agent ...

Elon Musk revealed his intense focus on Tesla's AI chip development, dedicating weekends to the AI5 project promising a 50x ...

Edge AI addresses high-performance, low-latency requirements by embedding intelligence directly into industrial devices.

Understanding GPU memory requirements is essential for AI workloads, as VRAM capacity--not processing power--determines which models you can run, with total memory needs typically exceeding model size ...

Here are our picks for the top 10 edge AI chips with a bright future across applications from vision processing to handling multimodal LLMs.

Waldorf synthesizers have a good reputation for their sound. The Protein establishes a new device class at a smaller price.

The optimized detection model is integrated into both a mobile application and a dedicated edge device, demonstrating that real-time waste detection can operate reliably without cloud connectivity.

No objectives succeed without data. Sustainability attributes scatter across messy text, images, and databases. Companies ...

Mapping the fast-growing market for AI processors indicates the startup boom has peaked — or is very close to it.

Discover the game-changing potential of local AI in 2026.