arXiv:2606.16332v1 Announce Type: cross Abstract: Modern CPUs increasingly integrate matrix extensions, such as Arm Scalable Matrix Extension (SME), that provide high-throughput matrix execution within the CPU. For LLM inference, however, these units are not a universal replacement for conventional CPU cores: prefill, decode, attention, and KV-cache operations expose different arithmetic intensities, vector behavior, and layout requirements, while SME units and CPU cores still compete for shared memory bandwidth. This paper studies this mismatch through a roofline-based characterization of SME
The rapid scaling of LLM inference demands optimized hardware utilization, making advanced CPU extensions like SME critically important for current and future AI applications.
Optimizing LLM inference performance directly impacts the cost, power consumption, and scalability of AI systems, influencing the trajectory of AI development and deployment.
The focus is shifting towards fine-grained optimization of LLM operations on heterogeneous CPU architectures, rather than solely relying on generalized high-throughput compute.
- · Chip designers (e.g., Arm)
- · Cloud providers
- · AI model developers
- · Edge AI device manufacturers
- · Companies with suboptimal hardware-software co-design
- · Generically optimized CPU architectures
- · Less efficient LLM inference solutions
Improved performance and efficiency of LLM inference on CPUs lead to lower operational costs for AI services.
This optimization could accelerate the deployment of powerful AI models on edge devices, reducing reliance on centralized cloud infrastructure.
Enhanced CPU-based LLM capabilities may diversify the AI compute landscape, potentially reducing the extreme demand on specialized accelerators like GPUs for certain workloads.
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Read at arXiv cs.AI