Joint demo highlights CXL reminiscence pool (as much as 100TiB) is the best scale up reminiscence resolution for fixing AI workload reminiscence wall situation, from each efficiency and TCO level of views
XConn Applied sciences (XConn), the innovation chief in next-generation interconnect know-how for high-performance computing and AI purposes, and MemVerge®, the chief in Large Reminiscence software program, introduced a joint demonstration of Compute Specific Hyperlink® (CXL®) reminiscence pool for breakthrough AI workload reminiscence scale-up on the 2025 OCP World Summit, October 13–16, in San Jose, California.
As AI purposes proceed to surge in scale and complexity, the business faces an pressing problem—the reminiscence wall. To energy the following technology of clever computing, a real reminiscence scale-up resolution is important. CXL reminiscence pooling, now commercially viable and quickly increasing, stands as the one confirmed path ahead. By enabling dynamic, low-latency, and high-bandwidth sharing of huge reminiscence assets throughout CPUs and accelerators, it breaks via conventional architectural limits. 100 TiB industrial CXL reminiscence swimming pools can be found in 2025 and even bigger deployments are on the horizon for 2026 and past.
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The demo will spotlight a CXL reminiscence pool, powered by the XConn Apollo change and MemVerge Gismo know-how, built-in into NVIDIA’s Dynamo structure and NIXL software program, to deal with the KV cache trade and offloading. It’s going to present the CXL reminiscence pool not solely an appropriate resolution to the reminiscence wall situation, but additionally a big efficiency enhance (> 5x) for AI inference workloads, compared with SSD. By combining the XConn Apollo change, the business’s first hybrid CXL/PCIe change, with MemVerge’s Reminiscence Machine X software program, the businesses will showcase how enterprises can obtain breakthrough scalability, efficiency, and effectivity for big AI inference and coaching fashions.
Demonstrations will likely be obtainable within the OCP Innovation Village Sales space 504, offering attendees with a number of alternatives to discover the joint resolution in motion. Through the occasion, XConn’s Jianping Jiang, Senior Vice President of Enterprise and Product, can even element the advantages of scale up reminiscence resolution for AI workload powered by XConn’s Extremely IO Transformer know-how through the session, “Co-Designing for Scale: CXL-Primarily based Reminiscence Resolution for Knowledge-Centric Workloads,” to be introduced throughout OCP on Wednesday, October 15 at 11:05 a.m.
“As AI workloads hit the reminiscence wall points, CXL reminiscence pool is the one viable reminiscence scale up resolution for at this time and the close to future. It not solely dramatically boosts AI workload efficiency but additionally offers vital TCO advantages,” stated Gerry Fan, CEO of XConn Applied sciences. “Our collaboration with MemVerge at OCP demonstrates how CXL reminiscence pool is a prepared for deployment resolution to be utilized to even probably the most demanding AI purposes.”
“AI is fueling a revolution in infrastructure design, and reminiscence is on the coronary heart of it,” stated Charles Fan, CEO and co-founder of MemVerge. “By pairing GISMO with the XConn Apollo change, we’re showcasing how software-defined CXL reminiscence can ship the elasticity and effectivity wanted for AI and HPC. This collaboration extends the probabilities of CXL 3.1 to assist organizations run bigger fashions sooner and with larger useful resource utilization.”
The joint demo will illustrate how MemVerge’s World IO-free Shared Reminiscence Objects (GISMO) know-how allows NVIDIA’s Dynamo and NIXL to faucet into large CXL reminiscence pool (as much as 100TiB in 2025) and function the KV Cache retailer for AI inference workloads, the place prefill GPUs and Decode GPUs work in synchrony to reap the benefits of the low latency and excessive bandwidth reminiscence entry to finish the computing. When mixed with XConn’s low-latency and excessive lane depend change cloth, the result’s a brand new class of reminiscence infrastructure able to supporting giant and scalable reminiscence pool measurement with decrease TCO, able to deal with the rising difficult work for AI inference, generative AI, real-time analytics, and in-memory databases.
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