Atomiq

No. This is a platform milestone announcement, not a commercial product launch. Atomiq is not yet available for customer deployment, and we’re not demonstrating Atomiq silicon at CES. This announcement provides early visibility into our architectural direction and long-term roadmap.

CES is where customers, partners, and investors look for directional signals, not just products. Atomiq reflects years of internal development and customer engagement, and announcing it now helps align the ecosystem around where edge AI is heading—and where Ambiq is investing.

Apollo is the foundation of Ambiq’s current edge AI success. Atomiq extends that foundation, introducing a new system architecture optimized for higher-performance, always-on AI workloads as models grow larger and more complex.
A simple way to think about it:
Apollo powers today’s edge AI deployments; Atomiq defines the next phase.

Absolutely. Apollo remains central to our portfolio and revenue today. Atomiq builds on Apollo’s success—it doesn’t replace it.

Atomiq introduces a system-level architecture designed from the ground up for AI workloads, including a dedicated NPU, while retaining Ambiq’s leadership in ultra-low-power operation through SPOT®. It’s about scaling intelligence without scaling power.

Ethos-U85 provides meaningful gains in AI performance and efficiency, and it aligns well with our SPOT® platform. Together, they enable larger models and more complex workloads at power levels that weren’t previously practical at the edge.

Applications that demand always-on intelligence, such as:
Conversational AR glasses Advanced wearables Smart cameras

They represent two ends of the edge AI spectrum—ultra-compact consumer devices and mission-critical industrial systems. Together, they show how Ambiq’s platforms scale across vastly different constraints and use cases.

We haven’t announced availability timelines. We plan to share additional technical details and roadmap updates beginning in March 2026 at Embedded World.

It represents the next step in pushing voltage, power efficiency, and memory architecture even further—enabling more advanced AI at lower energy budgets