AMD is laying out a more ambitious vision for the AI PC, and it goes beyond the usual mix of operating-system assistants and on-device inference demos. The company is now promoting what it calls the ...

Model selection, infrastructure sizing, vertical fine-tuning and MCP server integration. All explained without the fluff. Why Run AI on Your Own Infrastructure? Let’s be honest: over the past two ...

Artificial intelligence is rapidly moving beyond cloud servers and into the devices people use every day. Laptops, sm ...

Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory ...

Memories.ai is building a large visual memory model that can index and retrieve video-recorded memories for physical AI.

Nvidia's BlueField-4 STX reference architecture inserts a dedicated context memory layer between GPUs and traditional storage, claiming 5x token throughput and 4x energy efficiency for agentic AI ...

This article outlines the design strategies currently used to address these bottlenecks, ranging from data center systolic ...

This article explores that question through the lens of a real-world Rust project: a system responsible for controlling fleets of autonomous mobile robots. While Rust's memory safety is a strong ...

Inferencing at the edge has very different needs than training large language models or large-scale inferencing in AI data centers. Many edge devices run on a battery. They’re price-sensitive, and ...