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 ...
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 ...
Every ChatGPT query, every AI agent action, every generated video is based on inference. Training a model is a one-time ...
OpenClaw turns LLMs into autonomous agents that actually do stuff. Here is a hands-on reality check of running it on local hardware and burner phones.
At the end of 2025, the Institute of Artificial Intelligence of China Telecom (TeleAI), announced a major breakthrough: ...
Edge AI addresses high-performance, low-latency requirements by embedding intelligence directly into industrial devices.
Titled AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability, the study proposes a market-based ...
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