AI

Practical benchmarks showing faster inter-token latency when deploying Qwen3 models with vLLM, Kubernetes, and AWS AI Chips. Speculative decoding on AWS Trainium can accelerate token generation by up to 3x for decode-heavy workloads, helping reduce the cost per output token and improving throughput without sacrificing output quality. If you build AI writing assistants, coding agents, […]

The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability to monitor, track, and govern AI agents has become essential for operational sustainability. This is the journey of Rede

Once a $4 billion apparel juggernaut, Allbirds will rebrand as NewBird AI, a “GPU-as-a-Service” company. Hey, if you can’t beat ’em, join ’em.

The matte 4K QLED display reduces glare and delivers rich, accurate colors for art, photos, and fast streaming entertainment with the Fire TV experience built in.

Alexa+ can take real world actions, have natural conversations, and is built to understand how Italians really communicate.

A new study examines the impact of the rise of AI-generated websites on the internet—and found some surprising results.

An analysis by WIRED and Indicator found nearly 90 schools and 600 students around the world impacted by AI-generated deepfake nude images—and the problem shows no signs of going away.

Generative AI is reshaping how organizations approach productivity, customer experiences, and operational capabilities. Across industries, teams are experimenting with generative AI to unlock new ways of working. Many of these efforts produce compelling proofs of concept (POC) that demonstrate technical feasibility. The real challenge begins after those early wins. Although POCs frequently demonstrate technical feasibility,

Amazon SageMaker JumpStart provides pretrained models for a wide range of problem types to help you get started with AI workloads. SageMaker JumpStart offers access to solutions for top use cases that can be deployed to SageMaker AI Managed Inference endpoints or SageMaker HyperPod clusters. Through pre-set deployment options, customers can quickly move from model

Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to over-provisioning or performance bottlenecks, and the operational overhead of managing GPU resources efficiently. These pain points result in delayed time-to-market, suboptimal model performance, and inflated costs that can make