Create Pooled Memory
Make disaggregated capacity available across nodes for improved utilization and memory-intensive workloads.
● Products › Memory Expansion Servers
Scale AI applications efficiently with high-capacity memory server platforms engineered for demanding inference workloads.
● Why Big Memory Servers
Large AI models place heavy pressure on memory capacity and bandwidth. CXL-enabled memory infrastructure creates flexible shared capacity, helping reduce GPU waiting time and improve inference responsiveness.
A balanced memory architecture can extend existing accelerator investments while giving production systems room to scale.
↓ Download DatasheetMake disaggregated capacity available across nodes for improved utilization and memory-intensive workloads.
Support responsive real-time applications with consistent low-latency infrastructure.
Increase throughput and scalability while reducing memory-related compute bottlenecks.
Move KV cache workloads to a dedicated high-capacity CXL platform.
Reuse cached data intelligently to reduce repeated processing and improve throughput.
Support large memory configurations for demanding production inference environments.
Reduce idle compute time by keeping required data closer and readily available.
● Key Benefits
A purpose-built KV cache platform can store and reuse computed data outside constrained GPU memory. This reduces repeated work, improves response time and supports larger models, longer context windows and greater concurrency.
By expanding memory available to accelerated systems, organizations can use existing GPU resources more effectively and design clusters around sustained high-throughput inference.
Take Server Virtual Tour| 4U | Processor | PCIe Slots | Memory Capacity |
|---|---|---|---|
| + MemoryAI™ KV Cache Server | Dual AMD EPYC™ 9005 Series | 8× PCIe Gen5 x16 FHFL, 2× PCIe Gen5 x16 LP | Up to 11 TB DDR5 |
| + Altus XE4318GT-CXL | Dual AMD EPYC™ 9005 Series | 8× PCIe Gen5 x16 FHFL, 2× PCIe Gen5 x16 LP | High-capacity CXL expansion |
● Request a Callback
Discuss memory pooling, deployment planning, performance requirements and the right infrastructure approach for your AI or HPC environment.
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