Break Through Your AI Memory Scaling Limitations
Memory is a fundamental limitation in artificial intelligence (AI) deployments, especially for enterprise-scale AI inference. Overcome this challenge and get unprecedented performance, scalability, and cost-effectiveness with enterprise memory expansion and pooling technology.
Large AI Deployment
Memory Pain Points
The widening performance gap between processors and memory—known as the "memory wall"—is a significant challenge for memory-demanding applications. Unlike AI model training which is episodic and compute-intensive, AI inference is real-time, user-facing, and memory-dependent. Performance slows when memory-starved graphics processing units (GPUs) struggle to produce tokens and become idle while waiting for data.
Slow Data Transfer
The time it takes to move data between the GPU and memory (or across multiple GPUs) can create significant bottlenecks that lengthen training time.
Inference Latency
For AI inference that uses trained models, the memory wall can increase latency as the AI model accesses data from memory to make its predictions.
Reduced Throughput
If the memory system cannot keep up with the processing demands of inference requests, the overall throughput of the AI system will decline.
Scalability Challenges
Scaling AI models to serve a large number of users can run up against memory limitations, requiring more hardware and complex infrastructure to resolve.
Scale the AI Memory Wall & Resolve Memory Bottlenecks
AI inference requires compute infrastructure designed to handle continuous workloads, low latency, and high concurrency, all while keeping costs under control. Training large AI models require ultra-fast memory bandwidth which cannot keep up with the increasing compute processing demand.
With processors executing instructions faster than memory can supply the needed data for both cases, Penguin Solutions developed technology adopting the Compute Express Link® (CXL) protocol that facilitates breakthrough AI performance for emerging workloads and addresses memory-related bottlenecks while supporting an open ecosystem for data center accelerators and other high-speed enhancements.
What is CXL Technology?
CXL is an industry open-standard protocol that redefines how servers manage memory and compute resources. By enabling high-speed, low-latency connections between GPUs or central processing units (CPUs) and memory, CXL eliminates traditional AI data processing bottlenecks and unlocks new levels of lower cost scalability and computational performance for data-intensive workloads such as AI inference, agentic AI, and other emerging applications powered by AI.
Speed and accuracy drive competitive advantage. For organizations that require competitive insights faster, CXL-enabled memory solutions deliver game-changing capacity benefits:
• Faster data processing: Real-time analysis of massive datasets with minimal delay.
• Improved infrastructure efficiency: Optimized resource utilization and lower operational costs.
• Scalable, future-proof solutions: Seamlessly expandable memory to meet evolving data demands without costly infrastructure overhauls.
Keep Up With Advances in Accelerated Computing Workloads
With AI, high-performance computing (HPC), and machine learning (ML) requiring large amounts of high-speed memory that exceeds what conventional servers can accommodate, attempts to add more system memory via the traditional dual in-line memory module (DIMM) based parallel bus interface is problematic due to pin limitations on CPUs.
CXL-based solutions are more pin-efficient, which means more available possibilities for adding memory. Our 4-DIMM and 8-DIMM Add-In Cards (AICs) leverage this technology with advanced CXL controllers that eliminate memory bandwidth bottlenecks and capacity constraints for compute-intensive AI, HPC, and ML workloads.
Accelerate AI Inference with MemoryAI™
The MemoryAI KV Cache Server from Penguin Solutions is the industry’s first production-ready key-value (KV) cache server leveraging CXL memory to deliver high-capacity memory and support high-performance AI inference at scale.
Leveraging Penguin Solutions' high-density DIMM-based CXL AICs, the MemoryAI server enables seamless memory scaling. This ability to scale is essential for large models and long context which depend on the KV cache technique to facilitate high concurrency and low latency inferencing. MemoryAI seamlessly shares memory across GPU nodes and stores pre-computed keys and values, accelerating prompt prefixes when generating tokens.
Reach out to Penguin Solutions today to learn more about our CXL server products and explore how we can help you affordably scale the memory wall, unleash your AI initiatives, and turn your data into actionable insights faster.
The AI memory wall refers to the performance bottleneck that arises when the processing speed of GPUs and/or CPUs and accelerators outpaces the available memory bandwidth and capacity. This bottleneck limits the size and complexity of AI models that can be trained and deployed efficiently.
Scaling the AI memory wall involves improving data transfer efficiency between memory and processors to reduce latency and eliminate bottlenecks for compute-intensive tasks like AI inference and AI model training.
Because AI training and inference involve processing massive datasets, memory access delays can limit throughput and slow performance, especially for large-scale deep learning models.
As AI models grow in size and complexity, strategies with implemented scalable memory solutions such as CXL technology will be essential to keep training and inference times manageable and cost-effective.
CXL solves the memory wall problem by increasing memory capacity and bandwidth using CXL-attached memory. This approach allows processors to access data faster than their processing speed limit, providing coherent, low-latency access to a shared pool of memory by leveraging the high-speed PCIe interconnect.
Penguin Solutions addresses the AI "memory wall" challenge—where processor speed outpaces memory capacity and bandwidth—by offering MemoryAI KV Cache Server and CXL-based memory expansion technologies that enable scalable, low-latency, and cost-effective memory solutions for large-scale AI inference workloads, improving throughput, reducing latency, and supporting high concurrency through advanced CXL Add-In Cards and memory pooling techniques.
Talk to the Experts at Penguin Solutions
Reach out today and learn how we can help you maximize your memory expansion and pooling capabilities with lower-cost memory capacity scaling using CXL technology.