Accelerating AI with UALink: Open Memory Fabrics for Scalable Compute

Memory, not compute, is rapidly becoming the limiting factor for scaling modern AI across GPUs, accelerators, and CPUs. While HBM delivers enormous raw bandwidth, its limited capacity and strict locality quietly cap model size and context length, forcing over-buying of GPUs just to get more memory and leaving expensive compute underutilized. Combined with the high cost of HBM, memory-bound workloads such as LLM inference become more expensive and harder to run profitably as a service.

Fabric-attached memory powered by UALink decouples memory growth from GPU count, enabling independent scaling of memory and compute at near-HBM latency. By introducing a shared memory tier that supports pooling, disaggregation, and cross-node access, UALink brings mainstream DRAM into the accelerator domain as an open, scalable resource, lowering cost per token while unlocking larger models and richer contexts.

In this webinar, UnifabriX showcased its UALink-powered Memory over Fabrics™ platform, designed to accelerate large model workloads and substantially reduce the cost per token. Attendees gained insights into the UALink architecture and discovered how fabric-attached memory unlocks powerful new approaches to KV-cache optimization and RAG acceleration, delivering significant gains in inference speed and efficiency.

Presented by: Ronen Hyatt, CEO and Chief Architect, UnifabriX