Quantum ActiveScale Object Storage Introduces Industry-First Ranged Restore for Glacier-Class Archives, Delivering 5x Faster Restore Performance

Quantum Corp. announced new enhancements to the ActiveScale object storage platform that dramatically accelerate access to cold data stored in Glacier-class archive tiers. The updates introduce industry-exclusive Ranged Restore capabilities for its cold storage tier, powered by Quantum’s patented erasure-coded object-on-tape architecture.Customers can now restore only the specific byte ranges they need from large objects, rather than rehydrating entire files, reducing retrieval times and improving operational efficiency. The latest platform updates also deliver substantial performance gains when reading small objects from the cold tier, enabling restores more than five times faster. With these advancements, tape-based archives can now operate as responsive, query-ready data lakes designed for AI, analytics, and high-performance computing workloads.

“AI changes the value of archive data,” said Hugues Meyrath, CEO, Quantum. “Organizations need to store large volumes of data affordably, but they can’t afford to wait hours or days to retrieve the data that feeds training pipelines and analytic engines. With these new ActiveScale enhancements, we’re redefining what cold storage means, making massive, long-term archives readily accessible, intelligent, and primed for AI at EB scale.”

The new Ranged Restore feature enables customers to retrieve only the exact byte ranges needed from large objects stored in Glacier-class tape tiers. While traditional Amazon S3 Glacier*operations require full-object retrieval – often hundreds of gigabytes at a time – ActiveScale allows selective restore of just the required data segments. This capability reduces wait times, compute cycles, and egress impact, making archives more usable and operationally efficient. Quantum is the only provider currently offering this custom S3 Glacier extension for tape-based cold storage.

The newest ActiveScale platform version also introduces a new restore engine design that delivers more than 5X higher throughput for small object restores compared to previous ActiveScale versions. This performance boost is enabled by intelligent batching and ordering restore requests, accelerating high-volume retrieval workflows such as AI training. This enhancement is particularly impactful for AI model training, data validation jobs, compliance lookups, and automated pipeline-driven restores where responsiveness drives productivity and time-to-insight.

“Cold data is no longer offline data,” said Geoff Barrall, CPO, Quantum. “By eliminating the legacy limits of Glacier-class archives, ActiveScale turns tape into an active asset – fast, API-accessible, and ready for AI and analytics at scale.”

These enhancements further strengthen ActiveScale as the industry’s most flexible and future-ready cold data platform. By transforming archive-tier storage into a performant, selectively accessible, and AI-aligned asset, ActiveScale enables enterprises to unlock the full value of their data, no matter how long it has been stored or how large the archive becomes.