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.

Local AI concurrency perfromace testing at scale across Mac Studio M3 Ultra, NVIDIA DGX Spark, and other AI hardware that handles load ...

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 ...

Eight years after the first mobile NPUs, fragmented tooling and vendor lock-in raise a bigger question: are dedicated AI ...

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

As enterprises seek alternatives to concentrated GPU markets, demonstrations of production-grade performance with diverse ...

Agnes is actively fundraising at a valuation exceeding USD 100 million, with expansion plans targeting Indonesia, India, the ...