Inference is now the backbone of AI. Whether it’s powering a virtual assistant, a code companion or a real-time search agent, the need for low cost, high-performance inference continues to grow.

AmpereOne® M is built to meet that need at scale. With compute engines optimized for LLM operations, high memory bandwidth, and predictable performance under load, it delivers an efficient, scalable foundation for modern AI services.

Here are 5 ways AmpereOne® M is delivering better AI inference:

1. Predictable Latency, Even at Scale

Inference performance isn’t just about throughput. Consistency matters, especially for real-time applications. AmpereOne® M features up to 192 single-threaded cores, designed to minimize contention and deliver stable performance under load.

Whether you’re supporting multiple users, running concurrent models, or handling variable traffic patterns, AmpereOne® M provides reliable, low-latency responses at every scale.

2. Power Efficiency That Reduces Infrastructure Cost

Running inference around the clock can drive up both energy use and total cost of ownership. AmpereOne® M is optimized for performance per watt due to its high core density and efficient execution pipeline.

The result: more tokens per second, lower power per query, and increased efficiency, without compromising performance.

3. Built to Maximize Model Density

Today’s LLMs demand more than just compute. They need high memory bandwidth, large token buffers, and fast data movement across cores. AmpereOne® M is architected for exactly that, with:

• -Up to 192 cores per socket for highly scalable inference
• -12 channels of DDR5 delivering high memory bandwidth
• -Large, distributed caches that reduce memory bottlenecks

This architecture gives LLMs the space and speed they need, whether you’re scaling up context windows or deploying more complex model architectures.

4. Designed from the Ground Up for Cloud-Scale AI

Unlike traditional CPUs that were adapted from desktop-era designs, AmpereOne® M was engineered specifically for large-scale, Cloud Native workloads, including AI inference.

With single-threaded core architecture, memory hierarchy, and power management, AmpereOne® M was designed to provide:

• -Efficient scaling across thousands of cores
• -Smooth multi-tenant performance
• -Simple integration into existing cloud infrastructure

This is infrastructure for AI at scale, with no legacy overhead.

5. Optimized Software to Get the Most Out of Every Core

AmpereOne® M doesn’t just bring hardware performance. It’s backed by a growing software ecosystem designed to accelerate LLM deployment and inference at scale.

At the center is the Ampere® AI Optimizer (AIO), a tool that helps developers and platform teams transform, optimize and tune LLMs specifically for Ampere’s architecture. Whether you’re working with open-source models or custom builds, AIO streamlines quantization, layer fusion, and format conversion to deliver higher throughput with lower latency, without requiring a rewrite.

Ampere also maintains upstream contributions and ecosystem integrations with popular AI frameworks, ensuring that developers can bring models to production with minimal friction.

The Bottom Line:

LLM inference is only getting more critical and more demanding. AmpereOne® M offers a new approach: high-throughput, predictable and power-efficient inference, fully integrated into a modern, Cloud Native CPU.

With matrix math capabilities built directly into the core, a massively parallel architecture, and Cloud Native design choices throughout, AmpereOne® M is the CPU modern AI infrastructure can rely on.

Related: AmpereOne® M Product Brief

Disclaimer

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System configurations, components, software versions, and testing environments that differ from those used in Ampere’s tests may result in different measurements than those obtained by Ampere.

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