eWEEK Staff

Microsoft's MAI-Code-1.1-Flash eliminates local inference fees but recommends over 120GB RAM, raising hardware cost questions for enterprises.

IBM and Red Hat say Lightwell fixed more than 400 previously unknown Java library flaws as its enterprise remediation service expands.

Google, Meta, Microsoft, and other AI infrastructure companies pledged to cover their full data center energy costs instead of shifting them to other customers.

Argo-Bench found the top AI model fully solved just 34.8% of enterprise tasks, highlighting reliability gaps in autonomous AI agents.

OpenAI and Synopsys are building GPT-Synopsys to let AI agents operate chip design tools, but public customer benchmarks are still missing.

As AI adoption expands, organizations must make deliberate choices about where models are trained, tuned, and run for inference –and how those workloads are distributed across enterprise infrastructure. Hybrid AI strategies distribute AI workloads across data centers, cloud platforms, and edge environments to balance performance, scalability, governance, and cost.

Enterprise AI workloads require infrastructure designed for large-scale data processing and distributed computing. Organizations are modernizing AI data center infrastructure with GPU computing, high-speed networking, and scalable architectures to support AI training and inference.

Meta’s new enterprise AI platform brings agents, APIs, and developer tools together, but key deployment and governance details remain unclear.

Palo Alto says no single AI model found more than 40% of enterprise vulnerabilities, exposing major blind spots in AI-assisted security testing.

IonQ plans to connect its Superion 256 quantum processor to Nvidia GB200 GPUs in 2027, creating a new test bed for hybrid quantum-GPU computing.

Microsoft is extending enterprise AI security with Defender agent discovery and Purview-Entra controls for sensitive data moving to shadow AI services.

Xiaomi released MiMo-V2.6 Pro, Flash, and 9B AI models alongside reinforcement-learning resources, giving enterprises new open-weight deployment and testing options.