WEKA

WEKA is an AI-native, software-defined parallel storage platform (WekaFS) that presents a single namespace across NVMe flash with S3, POSIX, NFS, and SMB access. In 2026 its center of gravity shifted from "fast filesystem for AI training" to **inference-memory infrastructure**: the **Augmented Memory Grid** pools NVMe across nodes via a custom user-space RTOS and RDMA, and offloads the LLM **KV-cache** from GPU HBM to that persistent tier with sub-millisecond retrieval — a measured **7.5 million read IOPS**. It is one of the principal vendors defining the [Inference Context Memory Storage](/node/inference-context-memory-storage-icms) ("Tier 3.5") layer alongside VAST, Dell, and HPE.

WEKA is a load-bearing example of the thesis this index tracks — object/flash storage becoming an **active extension of GPU memory** rather than a cold archive. By letting serving engines page KV-cache to a shared RDMA-attached NVMe tier instead of pinning it in ~$100/GB HBM, WEKA lets a cluster hold context for multi-turn agentic sessions across days while keeping GPUs saturated. Its S3 interface makes that tier interoperable with the rest of the object-storage ecosystem rather than a proprietary island.

KV-cache offload / persistence for high-concurrency LLM inference; AI-training data delivery that keeps GPUs fed; RAG and agent-memory backends needing sub-ms retrieval at scale; mixed POSIX+S3 pipelines on one namespace.