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    <title><![CDATA[StreamingMeme — Hammerspace coverage]]></title>
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    <description><![CDATA[Articles mentioning Hammerspace.]]></description>
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      <title><![CDATA[StreamingMeme — Hammerspace coverage]]></title>
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    <item>
      <title><![CDATA[Hammerspace uses parallel NFS to resolve GPU cluster storage bottlenecks]]></title>
      <link><![CDATA[https://hammerspace.com/gpu-cluster-storage-bottlenecks-why-storage-architecture-is-the-real-limit-on-ai-training-throughput]]></link>
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      <pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_DELIVERY_AND_CDN]]></category>
      <description><![CDATA[Hammerspace outlines an architectural approach to resolving GPU data starvation in AI training clusters by utilizing parallel NFS and policy-driven data orchestration. The guide details how to diagnose storage-induced bottlenecks using telemetry and proposes a multi-layered storage strategy to improve GPU utilization.]]></description>
      <content:encoded><![CDATA[Hammerspace outlines an architectural approach to resolving GPU data starvation in AI training clusters by utilizing parallel NFS and policy-driven data orchestration. The guide details how to diagnose storage-induced bottlenecks using telemetry and proposes a multi-layered storage strategy to improve GPU utilization.]]></content:encoded>
      <dc:creator><![CDATA[Hammerspace]]></dc:creator>
      <author><![CDATA[Hammerspace]]></author>
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      <title><![CDATA[Hammerspace promotes global namespace to unify research data for AI]]></title>
      <link><![CDATA[https://www.scientific-computing.com/article/operationalising-research-data-ai]]></link>
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      <pubDate>Mon, 21 Sep 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_STREAMING_PLATFORMS_AND_INFRASTRUCTURE]]></category>
      <description><![CDATA[Hammerspace VP Floyd Christofferson discusses the use of a global namespace to orchestrate research data across distributed storage environments. The approach aims to reduce the time required to prepare siloed datasets for AI inference workloads by avoiding traditional data migration.]]></description>
      <content:encoded><![CDATA[Hammerspace VP Floyd Christofferson discusses the use of a global namespace to orchestrate research data across distributed storage environments. The approach aims to reduce the time required to prepare siloed datasets for AI inference workloads by avoiding traditional data migration.]]></content:encoded>
      <dc:creator><![CDATA[Scientific Computing World]]></dc:creator>
      <author><![CDATA[Scientific Computing World]]></author>
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      <title><![CDATA[Hammerspace uses NFSv4.2 and pNFS to mitigate enterprise storage vendor lock-in]]></title>
      <link><![CDATA[https://hammerspace.com/storage-vendor-lock-in-how-open-standards-and-abstraction-layers-protect-enterprise-infrastructure-investments]]></link>
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      <pubDate>Fri, 18 Sep 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_DELIVERY_AND_CDN]]></category>
      <description><![CDATA[Hammerspace advocates for the use of open standards like NFSv4.2 and pNFS to mitigate vendor lock-in in enterprise storage environments. The company proposes a software-based abstraction layer to enable data orchestration across multi-cloud and hybrid infrastructure without requiring proprietary client agents.]]></description>
      <content:encoded><![CDATA[Hammerspace advocates for the use of open standards like NFSv4.2 and pNFS to mitigate vendor lock-in in enterprise storage environments. The company proposes a software-based abstraction layer to enable data orchestration across multi-cloud and hybrid infrastructure without requiring proprietary client agents.]]></content:encoded>
      <dc:creator><![CDATA[Hammerspace]]></dc:creator>
      <author><![CDATA[Hammerspace]]></author>
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    <item>
      <title><![CDATA[Hammerspace advocates for MLPerf Storage benchmarks to reduce GPU idle time]]></title>
      <link><![CDATA[https://hammerspace.com/storage-performance-benchmarking-for-ai-infrastructure-what-the-numbers-actually-mean]]></link>
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      <pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_DELIVERY_AND_CDN]]></category>
      <description><![CDATA[Hammerspace argues that synthetic storage benchmarks often fail to predict AI workload performance, advocating instead for testing based on real-world I/O patterns like concurrent read bandwidth and metadata operations. The company emphasizes the importance of parallel file systems and pNFS for maintaining high GPU utilization in large-scale AI training environments.]]></description>
      <content:encoded><![CDATA[Hammerspace argues that synthetic storage benchmarks often fail to predict AI workload performance, advocating instead for testing based on real-world I/O patterns like concurrent read bandwidth and metadata operations. The company emphasizes the importance of parallel file systems and pNFS for maintaining high GPU utilization in large-scale AI training environments.]]></content:encoded>
      <dc:creator><![CDATA[Hammerspace]]></dc:creator>
      <author><![CDATA[Hammerspace]]></author>
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    <item>
      <title><![CDATA[Supermicro, Hammerspace, and Cloudian partner on unstructured data for AI]]></title>
      <link><![CDATA[https://siliconangle.com/2026/09/02/unstructured-data-hammerspace-cloudian-seagate-supermicro-supermicroopenstoragesummit/]]></link>
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      <pubDate>Wed, 02 Sep 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_DELIVERY_AND_CDN]]></category>
      <description><![CDATA[Supermicro, Hammerspace, Cloudian, and Seagate are collaborating on storage infrastructure solutions designed to manage unstructured data for AI training and inference. The initiative focuses on high-density hardware, S3-native object storage, and unified namespaces to automate data movement and governance for large-scale AI workloads.]]></description>
      <content:encoded><![CDATA[Supermicro, Hammerspace, Cloudian, and Seagate are collaborating on storage infrastructure solutions designed to manage unstructured data for AI training and inference. The initiative focuses on high-density hardware, S3-native object storage, and unified namespaces to automate data movement and governance for large-scale AI workloads.]]></content:encoded>
      <dc:creator><![CDATA[SiliconANGLE]]></dc:creator>
      <author><![CDATA[SiliconANGLE]]></author>
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      <title><![CDATA[Hammerspace uses metadata intelligence to automate petabyte-scale unstructured data management]]></title>
      <link><![CDATA[https://hammerspace.com/ai-driven-storage-management-how-metadata-intelligence-is-replacing-manual-data-operations]]></link>
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      <pubDate>Thu, 27 Aug 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_STREAMING_PLATFORMS_AND_INFRASTRUCTURE]]></category>
      <description><![CDATA[Hammerspace is promoting its Global Data Platform as an AI-driven solution for automating storage tiering and data orchestration across heterogeneous environments. The platform utilizes a unified metadata layer to manage unstructured data, aiming to optimize GPU saturation for AI training pipelines by pre-staging data at high speeds.]]></description>
      <content:encoded><![CDATA[Hammerspace is promoting its Global Data Platform as an AI-driven solution for automating storage tiering and data orchestration across heterogeneous environments. The platform utilizes a unified metadata layer to manage unstructured data, aiming to optimize GPU saturation for AI training pipelines by pre-staging data at high speeds.]]></content:encoded>
      <dc:creator><![CDATA[Hammerspace]]></dc:creator>
      <author><![CDATA[Hammerspace]]></author>
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    <item>
      <title><![CDATA[Hammerspace advocates for pNFS and NFS v4.2 in AI storage architectures]]></title>
      <link><![CDATA[https://hammerspace.com/ai-workload-storage-requirements-how-to-size-and-architect-infrastructure-for-gpu-clusters]]></link>
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      <pubDate>Mon, 17 Aug 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_STREAMING_PLATFORMS_AND_INFRASTRUCTURE]]></category>
      <description><![CDATA[Hammerspace provides a technical framework for architecting storage infrastructure for GPU clusters, focusing on parallel NFS and distributed metadata to mitigate I/O bottlenecks. The article outlines strategies for managing data ingestion, training, and checkpointing phases to optimize accelerator utilization in hybrid environments.]]></description>
      <content:encoded><![CDATA[Hammerspace provides a technical framework for architecting storage infrastructure for GPU clusters, focusing on parallel NFS and distributed metadata to mitigate I/O bottlenecks. The article outlines strategies for managing data ingestion, training, and checkpointing phases to optimize accelerator utilization in hybrid environments.]]></content:encoded>
      <dc:creator><![CDATA[Hammerspace]]></dc:creator>
      <author><![CDATA[Hammerspace]]></author>
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    <item>
      <title><![CDATA[Hammerspace uses metadata abstraction to unify hybrid storage without migration]]></title>
      <link><![CDATA[https://hammerspace.com/global-namespace-storage-unifying-enterprise-data-across-hybrid-infrastructure-without-migration]]></link>
      <guid isPermaLink="false">477a7140-5e3e-409f-80bb-ea15dea2e15c</guid>
      <pubDate>Tue, 04 Aug 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[VIDEO_DELIVERY_AND_CDN]]></category>
      <description><![CDATA[Hammerspace has detailed its global namespace storage architecture, which unifies distributed data across hybrid cloud and on-premises environments without requiring manual migration. The technology utilizes metadata-driven orchestration to improve performance and GPU utilization for AI and high-performance streaming workflows.]]></description>
      <content:encoded><![CDATA[Hammerspace has detailed its global namespace storage architecture, which unifies distributed data across hybrid cloud and on-premises environments without requiring manual migration. The technology utilizes metadata-driven orchestration to improve performance and GPU utilization for AI and high-performance streaming workflows.]]></content:encoded>
      <dc:creator><![CDATA[Hammerspace]]></dc:creator>
      <author><![CDATA[Hammerspace]]></author>
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