{"title":"Cache \/ Expansion","description":"\u003ch2\u003eBeyond DRAM: The Next Frontier of AI Server Memory\u003c\/h2\u003e\n\n\u003cp\u003eThe memory wall is real. As AI models grow larger and inference workloads demand that more model weights, KV caches, and feature vectors live in fast memory simultaneously, the physical limits of DRAM — slot counts, power envelopes, and cost per gigabyte — become genuine infrastructure constraints. The teams building the most capable AI systems today are the ones who have moved beyond thinking about memory as a fixed resource and started treating it as an expandable, tiered architecture.\u003c\/p\u003e\n\n\u003cp\u003eCXL (Compute Express Link) memory expansion, storage cache accelerators, and persistent memory technologies represent the current frontier of this architectural evolution. They allow AI servers to access memory capacities that would be physically impossible with DRAM alone, at latencies and bandwidths that make them practical for real AI workloads — not just theoretical benchmarks. DVUN's Cache \/ Expansion collection brings these technologies to AI infrastructure teams who are ready to push past the limits of conventional memory architecture.\u003c\/p\u003e\n\n\u003ch3\u003eTechnologies in This Collection\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eCXL Memory Expansion Modules:\u003c\/strong\u003e PCIe-attached memory expansion using the CXL 1.1\/2.0 protocol. Add 256GB to 2TB of additional memory capacity per module, accessible at near-DRAM latency from the host CPU. Ideal for large language model inference servers that need to hold multiple model instances in memory simultaneously.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eStorage Cache Accelerators:\u003c\/strong\u003e NVMe-based caching appliances that sit between your storage network and your compute nodes, caching hot data in fast local flash to reduce storage network traffic and improve effective I\/O throughput for training workloads.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePersistent Memory (PMEM):\u003c\/strong\u003e Byte-addressable persistent memory that combines the capacity of storage with the access speed of DRAM. Useful for checkpoint staging, feature store caching, and workloads that benefit from memory that survives a power cycle.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGPU Memory Extension:\u003c\/strong\u003e Software and hardware solutions that extend effective GPU memory capacity by transparently tiering between GPU VRAM, system DRAM, and NVMe storage — enabling larger models to run on existing GPU hardware.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eWhen Cache \/ Expansion Changes the Equation\u003c\/h3\u003e\n\n\u003cp\u003e\u003cstrong\u003eThe 70B Model Inference Problem:\u003c\/strong\u003e You need to serve a 70B parameter model in production, but your inference servers only have 80GB of GPU VRAM per card. Loading the full model requires either model parallelism across multiple GPUs or a memory architecture that can hold model weights in fast system memory and stream them to the GPU on demand. CXL memory expansion makes the latter practical — giving your inference server 512GB to 2TB of fast memory for model weight storage without the complexity of multi-GPU model parallelism. See our \u003ca href=\"\/collections\/memory-kits\"\u003eMemory Kits\u003c\/a\u003e for the DRAM foundation this architecture builds on.\u003c\/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eThe Training Checkpoint Bottleneck:\u003c\/strong\u003e Your training job writes checkpoints every 1000 steps, and each checkpoint write takes 3 minutes because it's going to network storage. Persistent memory or a local storage cache accelerator can reduce this to under 30 seconds by staging checkpoints locally before asynchronously replicating to shared storage. Pair with our \u003ca href=\"\/collections\/nvme-storage\"\u003eNVMe Storage\u003c\/a\u003e for the local storage tier that completes this architecture.\u003c\/p\u003e\n\n\u003ch3\u003eSpecifications Overview\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003eCXL memory: 256GB to 2TB per module, CXL 1.1\/2.0 protocol, PCIe Gen5 interface\u003c\/li\u003e\n\u003cli\u003eCXL latency: 200–300ns (vs. 80–100ns for local DRAM)\u003c\/li\u003e\n\u003cli\u003eStorage cache: 6.4TB to 25.6TB NVMe cache capacity, up to 20GB\/s throughput\u003c\/li\u003e\n\u003cli\u003ePersistent memory: 128GB to 512GB per module, byte-addressable\u003c\/li\u003e\n\u003cli\u003eGPU memory extension: supports models up to 10x GPU VRAM capacity on select configurations\u003c\/li\u003e\n\u003cli\u003eInterface: PCIe Gen4\/Gen5, CXL 1.1\/2.0, DDR5 DIMM slot (PMEM)\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eExpand What's Possible. Don't Replace What Works.\u003c\/h3\u003e\n\u003cp\u003eThe best infrastructure investments are the ones that extend the value of what you've already built. DVUN's Cache \/ Expansion collection lets you push your existing AI servers further — more memory, faster data access, larger models — without a full platform replacement. \u003ca href=\"\/pages\/request-a-quote\"\u003eRequest a quote\u003c\/a\u003e for memory expansion architecture design, or contact our team to discuss which technology is the right fit for your specific workload constraints.\u003c\/p\u003e","products":[],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0671\/0525\/9582\/collections\/cache-expansion.png?v=1782104710","url":"https:\/\/dvun.com\/collections\/cache-expansion.oembed","provider":"DVUN","version":"1.0","type":"link"}