Artificial intelligence (AI) workloads are advancing at a phenomenal rate, placing unprecedented strain on traditional computing infrastructure. The central challenge lies in the massive memory footprints and data-hungry nature of modern AI. Traditional interconnects create performance bottlenecks that limit how efficiently processors can access the vast amounts of data they need.
Compute Express Link (CXL) has emerged as a transformative solution. It’s an open industry interconnect designed to bridge these performance gaps, enabling more efficient resource utilization and paving the way for the next generation of AI systems.
What are the Bottlenecks Holding Back Modern AI?
Today’s AI and machine learning workloads introduce unique infrastructure challenges that expose the limitations of legacy, siloed architectures.
These problems directly impact performance, scalability, and cost; infrastructure challenges include:
- Massive Memory Footprints: Training and inference for large-scale models require terabytes of memory, often exceeding the capacity available in a single server node.
- Data Transfer Latency: CPUs, GPUs, and specialized AI accelerators must work in concert, but data transfer bottlenecks between them introduce latency and hinder overall performance.
- Resource Inefficiency: Siloed infrastructure frequently leads to stranded resources. A server may have underutilized compute cores because it lacks sufficient memory for a particular job, or vice-versa, leading to a poor return on investment.
- Storage Integration Delays: High-speed storage needs to be accessible as an extended memory tier, but moving large datasets and model checkpoints between storage and system memory remains a slow process.
Traditional PCIe-based architectures can’t fully resolve these issues due to their lack of memory coherency and higher latency. CXL was developed specifically to address these shortfalls.
What Is Compute Express Link?
CXL is a high-speed, cache-coherent interconnect protocol built on the PCIe physical layer. While it uses the same physical connections, it’s designed specifically for the demands of heterogeneous computing. It provides a unified interface that allows CPUs, GPUs, accelerators, and memory devices to share memory efficiently and coherently.
Its core functionality is enabled through three distinct protocols:
- CXL.io: This protocol handles standard I/O communication, device discovery, and configuration, functioning much like the familiar PCIe standard.
- CXL.cache: This protocol allows an attached device, such as an AI accelerator, to coherently cache memory from the host CPU. This ensures data consistency across the system without complex software management.
- CXL.mem: This protocol enables a host CPU to access memory that is physically attached to an external device, effectively treating it as part of its own memory space.
Together, these protocols create a powerful and flexible framework for building composable disaggregated and memory-centric architectures.
How Does CXL Transforms AI Infrastructure?
By breaking down the rigid barriers between compute, memory, and peripherals, CXL enables a more dynamic and powerful approach to building and managing systems for AI workloads.
1. Memory Pooling and Expansion AI
training jobs often fail due to insufficient memory on an individual node, but CXL addresses this issue by enabling memory to be pooled and shared across devices. It allows for the creation of large memory pools that can be dynamically allocated to different processors or accelerators as needed. Additionally, it enables CPUs and GPUs to share a single, unified memory pool without requiring redundant data copies. This approach reduces stranded memory and significantly lowers the total cost of ownership (TCO) by improving resource utilization.
2. Tiered Memory for Optimal Performance
AI systems benefit from a combination of high-bandwidth DRAM for performance and more cost-effective memory tiers for capacity. CXL makes this architecture seamless by using fast DRAM as the primary tier for performance-critical operations while attaching CXL-based memory expanders as a lower-cost, high-capacity second tier. This facilitates smooth data migration between tiers to balance speed, capacity, and cost-efficiency.
3. Enabling Composable Disaggregated Infrastructure (CDI)
CXL is a foundational technology for composable systems, where compute, memory, and storage resources can be disaggregated and provisioned on-demand to fit a specific workload. This allows you to flexibly assign the precise amount of acceleration and memory required for a given task and dynamically scale memory resources for AI inference clusters without having to overprovision hardware. The result is a more agile and responsive data center CDI that can adapt to changing application demands.
What are the Strategic Benefits of CXL for AI?
Adopting CXL provides tangible advantages for anyone building or deploying AI systems.
- Performance Scaling: It reduces the critical bottlenecks between CPUs, GPUs, and memory, unlocking greater performance from your hardware.
- Improved Efficiency: It drives higher utilization of both memory and compute resources, leading to a better return on investment and lower operational costs.
- Enhanced Scalability: It provides a clear path to supporting large-scale AI training with multi-terabyte memory footprints.
- Greater Flexibility: It enables disaggregated infrastructure that adapts to your workloads, not the other way around.
- Future-Proofing: It offers a standardized path toward next-generation, heterogeneous, and AI-optimized data centers.
Rapid Industry Adoption and the Road Ahead
Across the technology industry, server vendors, GPU and accelerator manufacturers, and major cloud providers are all integrating CXL into their product roadmaps. Meanwhile, the CXL Consortium continues to advance the standard, with CXL 2.0 and 3.0 expanding capabilities to include fabric switching, enhanced memory pooling, and global coherency to support even larger, more complex compute environments.
Bottomline, AI requires a fundamental change in how we architect computer systems. The traditional model of siloed resources is no longer sufficient. CXL provides the essential backbone for this transformation, enabling the memory-centric and composable architectures needed for next-generation artificial intelligence. By bridging the critical gaps in today’s infrastructure, CXL is set to become a cornerstone technology for the future of computing.
SMART Modular Technologies helps customers around the world enable AI and high-performance computing (HPC) through the design, development, and advanced packaging of integrated memory solutions. Our portfolio ranges from today’s leading edge memory technologies like CXL to standard and legacy DRAM and Flash storage products. For more than three decades, we’ve provided standard, ruggedized, and custom memory and storage solutions that meet the needs of diverse applications in high-growth markets. Contact us today for more information.
