AWS benchmarks show 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. This reduces the cost per output token and improves throughput without sacrificing output quality. Decode-heavy workloads often dominate inference costs because tokens are generated sequentially in autoregressive decoding. Speculative decoding addresses this bottleneck by allowing a small draft to guide generation.

• Showcases up to 3x faster token generation for decode-heavy workloads.
• Reduces cost per token while improving throughput without quality loss.
• Demonstrates deployment of Qwen3 with vLLM, Kubernetes, and AWS AI Chips.
• Addresses memory-bandwidth bottlenecks in autoregressive decoding.

GenAI chatbots are increasingly integrated into health and medical research workflows, offering researchers new tools to enhance efficiency and knowledge translation. Their practical application across the broader health research landscape remains complex and evolving. Health and medical researchers engage with complex study designs, theoretical frameworks, and population needs, which require thoughtful, effective, and responsible use of AI tools. The 10-chapter guide serves as a practical, evidence-informed resource for health and medical researchers. The framework aims to support safe and effective GenAI use throughout the scientific process.

• Clarifies how GenAI chatbots fit into health research workflows.
• Outlines responsible prompt engineering across study designs and populations.
• Offers a practical, evidence-informed resource in a 10-chapter format.
• Addresses evolving and complex use of AI tools in health research.

Tealium announced the launch of its AI Partner Ecosystem, a network of pre-built connectors that enable enterprises to activate AI models instantly with enriched, labeled, and contextual data starting at collection. The ecosystem unifies real-time context, data orchestration, and activation to create a continuous AI feedback loop across the enterprise. As organizations move from experimentation to production, the challenge is operationalizing AI beyond model building, given delayed, fragmented data and disconnected activation layers. Tealium's ecosystem addresses this gap by unifying real-time context and activation, enabling enterprises to use AI at the point of data collection. The goal is to deliver real-time, enterprise-scale AI outcomes.

• Launches the AI Partner Ecosystem with pre-built connectors.
• Connects real-time context, data orchestration, and activation.
• Enables instant activation of AI models across enterprise apps.
• Addresses the gap between experimentation and production.