Lenovo unveils Qira personal AI super-agent: cross-device

What happened Lenovo announced Lenovo Qira and Motorola Qira at CES 2026 (Jan 6). Qira is a system-level, cross-device "personal ambient intelligence" that learns context, surfaces proactive suggestions, and can act on a user's behalf across PCs, phones, tablets, and wearables. Lenovo says Qira supports on-device model execution, hybrid local-to-cloud orchestration, and partner integrations with Microsoft Azure, Stability AI, Notion, and Perplexity. (Lenovo; The Verge)

Infrastructure context At the same keynote, Lenovo unveiled the "Lenovo AI Cloud Gigafactory with NVIDIA," a joint program to pre-integrate liquid-cooled hybrid infrastructure and NVIDIA-accelerated systems. The program aims to cut time-to-production to weeks for AI cloud providers. Messaging highlights time-to-first-token speed, rack-scale NVL72 systems, and full lifecycle factory services to scale trillion-parameter workloads. (Lenovo; Reuters)

Implications Together, these announcements link a consumer-facing, always-on agent strategy to enterprise-scale infrastructure. Qira's hybrid design reduces latency and keeps more data local. The gigafactory program shortens cloud deployment cycles. Lenovo says Qira will roll out on select devices in Q1 2026 and includes opt-in memory and visible controls to address privacy and retention concerns. (Lenovo; The Verge)

Why It Matters

• Plan for hybrid deployments: Qira mixes on-device models with cloud models—update device provisioning and MDM policies to support local ML workloads and orchestration.
• Revisit privacy and governance: opt-in memory, visible recording indicators, and fused cross-device knowledge require updated consent workflows, retention settings, and audit controls.
• Adjust procurement and timelines: Lenovo and NVIDIA's gigafactory aim to cut time-to-production to weeks—cloud and infrastructure teams should reassess procurement, capacity planning, and integration windows.
• Budget for capable endpoints: Lenovo notes better Qira performance on machines with larger RAM; plan for devices with sufficient memory and local inference capacity to meet latency and UX targets.