Enfabrica Corporation, an industry leader in high-performance networking silicon for artificial intelligence (AI) and accelerated computing, today announced the availability of its Elastic Memory Fabric System "EMFASYS", a transformative hardware and software solution designed to dramatically improve the compute efficiencies in large-scale, distributed, memory-bound AI inference workloads. EMFASYS is the first commercially available system that integrates high-performance Remote Direct Memory Access (RDMA) Ethernet networking with an abundance of parallel ComputeExpressLink (CXL) based DDR5 memory channels. The solution provides AI compute racks with fully elastic memory bandwidth and memory capacity, in a standalone appliance reachable by any GPU server at low, bounded latency over existing network ports.

Generative, agentic, and reasoning-driven AI workloads are growing exponentially—in many cases requiring 10 to 100 times more compute per query than previous Large Language Model (LLM) deployments and accounting for billions of batched inference calls per day across AI clouds. The EMFASYS solution addresses the critical need for AI clouds to extract the highest possible utilization of GPU and High-Bandwidth-Memory (HBM) resources in the compute rack while scaling to greater user/agent count, accumulated context, and token volumes. It achieves this outcome by dynamically offloading HBM to commodity DRAM using a caching hierarchy, load-balancing token generation across AI servers, and reducing stranding of expensive GPU cores. When deployed at scale with Enfabrica's EMFASYS remote memory software stack, the solution enables up to 50% lower cost per token per user, allowing foundational LLM providers to deliver significant savings in a price/performance tiered model.