onedrive sync issues
Hi We use OneDrive for Business and need a solution for sharing files and folders among a team while maintaining proper access permissions. Currently, each team member's Desktop and Documents folders are synced with their own personal OneDrive for Business account. In addition, we have a separate common OneDrive account that has been added to all team members' PCs, and all team members have Owner permissions on that account. When a user creates a folder or saves a file in the shared OneDrive location, it is actually being created under the common OneDrive account, not in the user's personal OneDrive. While this allows everyone to access the same files, we frequently experience synchronization conflicts and sync issues between users. What is the Microsoft-recommended approach for this type of collaboration? Is using a common OneDrive account with multiple owners a supported best practice, or should we instead use a SharePoint document library (or another Microsoft 365 solution) for shared team files, permissions, and reliable synchronization? ThanksHow to get rid of old Onenote files in Onedrive
I've been using Onenote for many, many years and over that time I have collected several unused files that seem to be un-deletable on Onedrive. I have deleted some of them within Onedrive from time to time but the names seem to remain. Here is what I see. When I click "Files" in One drive I see folders and under that documents which contains my only current Onenote database. The one I use. But, then I click "home" and see six old unused data base files that cannot be deleted. When I click on some of them the screen says "Sorry, something went wrong - Item may have been deleted by another person" Another file will say "This item might not exist or has been deleted". I am the only one who uses this account. I'm not sure what HOME is or what its function is as it doesn't show any folders or files that are active. It only shows these abandoned Onenote databases that nothing can be done with. So, Is there a way to clean this up or is it just not worth the effort. Since I don't know what the function of Home is I'm not sure if it matters. Thanks for any enlightenment.Onedrive personal vault problems
I'm having problems with opening my personal vault. The cloud icon at the bottom right of my screen has a red X and all I get is an error saying my personal vault isn't updated. I can't access any of the settings for OneDrive or Personal Vault. I downloaded an update, but when I tried to install it I got an error message saying I was using a newer version and must delete it before continuing. What do I need to do to get my OneDrive and/or Personal Vault working again?
OneDrive desktop client does not start after complete uninstall, cleanup and reinstall
Hello, I have a problem with the OneDrive desktop client on Windows 11. Issue The OneDrive desktop client does not start at all. OneDrive.exe exists in: C:\Program Files\Microsoft OneDrive\OneDrive.exe Double-clicking OneDrive.exe does nothing. No sign-in window appears. No OneDrive cloud icon appears in the system tray. No error message is shown. Running: "C:\Program Files\Microsoft OneDrive\OneDrive.exe" /verbose returns immediately with exit code 0. Running: "C:\Program Files\Microsoft OneDrive\OneDrive.exe" /? also produces no output. What I already tested DISM completed successfully. SFC completed successfully. OneDrive was uninstalled. Remaining OneDrive program folders were deleted. Local OneDrive configuration folders were removed/renamed. OneDrive registry entries were cleaned. Old account configuration was removed. A previously connected work/school account was removed from Windows. OneDrive was installed again from the latest installer. The issue remains exactly the same. Additional findings The file OneDrive.exe is present after installation. The modern OneDrive Photos (Beta) app starts normally. The classic OneDrive synchronization client does not start. Searching for "OneDrive" in the Start menu only shows the OneDrive app, not the classic desktop client. In some cases only a "OneDrive Sync Service" process appears in Task Manager. No useful OneDrive application errors are logged in Event Viewer. Important test I created a completely new local Windows test user account. The problem occurs there as well: OneDrive.exe does not start. No sign-in window appears. No cloud icon appears. Therefore the issue does not appear to be limited to my original Windows user profile. Data status My local data is still intact on a separate drive: E:\OneDrive The problem only affects launching the OneDrive desktop client. Could you help investigate why the OneDrive desktop application exits immediately without showing any UI or sign-in experience? Thank you.
OneDrive is soooooooooooooooooooooo slow!
Why is OneDrive for Business so unacceptably slow? I've had a case open with Microsoft Support since February and to date they have involved several different teams, run numerous diagnostics and tests remotely on my computer, but still haven't been able to improve let alone resolve the very poor download speeds we are getting across the company. They can see the issue with their own eyes, and the under-performing tests are appearing in their diagnostic logs, but for whatever reason they cannot fix their own systems! We have a fibre-optic leased line, 100Mbit over a 1Gbit bearer. Running a speed test, I am getting the full 100Mbit up and down, and yet when I am downloading a file (whether it's a few MB or a few hundred MB) from OneDrive, the download rate hovers between 500Kb/s and 800Kb/s. Uploading the OneDrive for Business is fine - the upload rate is between 7Mb/s and 9Mb/s. It's just the download rate that's the issue. Whenever any of our users save anything to OneDrive and the document is no longer cached, they have to wait for what feels like an eternity for the file to be downloaded before it can be opened. If they're opening the file via an application, then that application will hang and stop responding until the file has been downloaded from their OneDrive. Before we switched to Microsoft 365, we hosted everything locally. Anything we tried to open would do so almost instantly. But using OneDrive feels like we have travelled back in time to the days of 56K/ISDN. If this is how OneDrive has been designed, then it should not be labelled as a business service. There are other solutions out there from competitors that outperform OneDrive, and unfortunately if these issues are not resolved soon, I doubt we will be the only business who migrates away from OneDrive or even Microsoft altogether.Scaling AI Storage: Azure Managed Lustre excels in MLPerf Storage v3.0
In large scale AI clusters storage is responsible for delivering training data fast enough to keep accelerators busy and completing checkpoint saves and recoveries quickly enough to avoid costly stalls. MLPerf Storage is an industry-standard benchmark developed by MLCommons that reproduces the I/O patterns of different stages of AI pipelines covering training, checkpointing, context cache and vector database scenarios, reporting how efficiently a storage system can sustain the I/O rates required by the workload maximizing accelerators throughput without introducing bottlenecks. Azure submitted results in the MLPerf Storage v3.0 round, and its training and checkpointing results rank as the top submissions among major hyperscalers, in both demonstrated scale and sustained performance. In checkpointing, Azure Managed Lustre reached 642.2 GiB/s of write throughput and 489.6 GiB/s during recovery, showing the highest peak write performance for both the 405B and 1T model configurations among hyperscalers and public cloud submissions. In training, Azure Managed Lustre demonstrated the ability to sustain the required accelerator utilization with hundreds of GiB/s across five different tiers and file systems ranging from 40 TiB to the largest system presented in the submission, a 25 PiB file system in a single POSIX namespace. These peer-reviewed results demonstrate that Azure Managed Lustre is a service built for the scale AI now demands and that can sustain the storage performance required to keep AI accelerators highly utilized across different stages of AI workloads. What the Benchmark Measures MLPerf Storage answers one question: how effectively can a storage system keep AI accelerators highly utilized and operating efficiently? In a modern AI cluster, accelerators are among the most constrained and expensive resources. Every second they wait for training samples or checkpoint I/O reduces the productive value of provisioned compute. MLPerf Storage isolates the storage path and measures whether it can sustain the I/O rate required by a representative AI workload. The benchmark has an OPEN and a CLOSED division. In the CLOSED division, workload code and benchmark parameters are standardized, while submitters may tune the storage system within the benchmark rules. This constraint reduces variation outside the storage path and makes results more directly comparable across submitters. Azure submitted every reported result in CLOSED division. Training category The training category evaluates whether a storage system can sustain the data-delivery rate required to keep simulated accelerators utilized throughout an extended time window, rather than only demonstrating a short burst of peak throughput. Why it matters: accelerators represent the widest portion of an AI cluster’s cost, and storage stalls reduce the productive value of provisioned compute. Because the benchmark evaluates simulated accelerator utilization rather than raw bandwidth alone, a passing score indicates that the storage path sustained the required workload’s data-delivery requirements without becoming a bottleneck. Checkpointing category The checkpointing category measures how quickly a storage system can write model state at a synchronization barrier and read that state during recovery, including recovery on different client hosts. These operations model the storage demands involved when a distributed training job resumes after a failure. Why it matters: checkpoint writes pause progress across a distributed training job, making checkpoint duration a shared overhead for the cluster with accelerator stalling. Faster writes let teams checkpoint more often and reduce the work at risk without significantly affecting accelerator utilization, while faster recovery reads shorten the time required to resume a disrupted job. What Was Tested Every result used Azure Managed Lustre, a managed parallel file system that presents clients with a single POSIX namespace. Testing covered five service tiers and file-system capacities from 40 TiB to approximately 25 PiB. MLPerf Storage generates workload I/O from standard client nodes rather than GPU nodes, and the submission used publicly available Azure virtual machine SKUs, network configurations, deployment patterns, and service interfaces. Results were produced across multiple Azure regions using configurations available to customers today. Together with the published system descriptions and configuration details, these results provide a practical reference for evaluating Azure Managed Lustre performance. In the training category, Azure Managed Lustre sustained throughput and maintained simulated accelerator utilization above 90% for five consecutive hours on the UNET3D workload with 13 distinct system configurations. The submitted configurations ranged from tens of TiB to tens of PiB and delivered sustained throughput in the hundreds of GiB/s. The five-hour result demonstrates predictable storage performance over an extended benchmark run rather than a short-lived burst. AMLFS delivers target performance across all service tiers, enabling customers to optimize capacity and performance for each workload without compromising accelerator utilization, as shown in Figure 1. Figure 1, AMLFS UNET3D Training performance (B200) across different bandwidth density configurations Most relevant to highlight is how Azure Managed Lustre delivers at more than 96% the nominal bandwidth density across all tiers tested in training category, and how the results, taking AMLFS 125 as a tier, scale almost linearly increasing the number of accelerators (Figure 2). Figure 2, AMLFS UNET3D Training performance (B200) delivered bandwidth density and scalability. In checkpointing category, the benchmark covered four Llama3 scales, from an 8B model with a 105 GB checkpoint to a 1T-class model with an 18 TB checkpoint, and Azure submitted results for all four. Within those benchmark rules, Azure Managed Lustre reached up to 642 GiB/s of checkpoint write throughput and up to 490 GiB/s during recovery, enabling the tested checkpoints to be written and restored in tens of seconds or less. For checkpointing, AMLFS 125 has been picked as example tier, but similar results are expected, as demonstrated in the training scenario, with different AMLFS tiers. Figure 3, AMLFS checkpointing performance across different model size. The published results give the AI community a peer-reviewed view of Azure Managed Lustre under modern AI workloads, including sustained throughput, predictable performance, and deployment choices across service tiers. Full system descriptions and performance numbers are available in the MLCommons MLPerf Storage v3.0 results tables. Five Tiers, 20 to 500 MB/s per TiB, One Right Size for Every AI Workload Results span five Azure Managed Lustre service tiers, from 20 MB/s per TiB to 500 MB/s per TiB, showing how the tested performance extends across the range of configurations available for demanding AI workloads. That breadth is deliberate because AI infrastructure requirements vary by model size, accelerator count, data volume, and I/O pattern; there is no single correct storage configuration. Results across five tiers show how customers can balance throughput, capacity, and cost as models, datasets, and clusters grow. Correct sizing can reduce storage bottlenecks and shorten time to results while retaining the durability and resiliency of a managed cloud file system. Getting Started with Azure Managed Lustre To evaluate Azure Managed Lustre for an AI or HPC workload, review the Azure Managed Lustre documentation for service capabilities, planning guidance, and deployment options. Then use the Azure portal deployment guide to configure and create a file system. For deployment examples, explore the AI Infrastructure on Azure GitHub repository. It provides reference architectures, best practices, and templates for AI and HPC environments, including examples built with Azure CycleCloud and Azure Kubernetes Service. Together with the resources below, these examples show how Azure Managed Lustre can serve as a high-performance shared storage layer for AI workloads. Learn More Azure Resources Azure Managed Lustre Azure Managed Lustre MLPerf Storage v3.0 results Azure AI Infrastructure AI Infrastructure on Azure GitHub repository Azure CycleCloud Azure Kubernetes Service MLPerf Resources MLCommons MLPerf Storage MLPerf Storage GitHub repository Benchmark MLPerf Storage | MLCommons V3.0 Results GitHub Repository of MLPerf Storage v3.0 resultsAnnouncing Native NVMe in Windows Server 2025: Ushering in a New Era of Storage Performance
We’re thrilled to announce the arrival of Native NVMe support in Windows Server 2025—a leap forward in storage innovation that will redefine what’s possible for your most demanding workloads. Modern NVMe (Non-Volatile Memory Express) SSDs now operate more efficiently with Windows Server. This improvement comes from a redesigned Windows storage stack that no longer treats all storage devices as SCSI (Small Computer System Interface) devices—a method traditionally used for older, slower drives. By eliminating the need to convert NVMe commands into SCSI commands, Windows Server reduces processing overhead and latency. Additionally, the whole I/O processing workflow is redesigned for extreme performance. This release is the result of close collaboration between our engineering teams and hardware partners, and it serves as a cornerstone in modernizing our storage stack. Native NVMe is now generally available (GA) with an opt-in model (disabled by default as of October’s latest cumulative update for WS2025). Switch onto Native NVMe as soon as possible or you are leaving performance gains on the table! Stay tuned for more updates from our team as we transition to a dramatically faster, more efficient storage future. Why Native NVMe and why now? Modern NVMe devices—like PCIe Gen5 enterprise SSDs capable of 3.3 million IOPS, or HBAs delivering over 10 million IOPS on a single disk—are pushing the boundaries of what storage can do. SCSI-based I/O processing can’t keep up because it uses a single-queue model, originally designed for rotational disks, where protocols like SATA support just one queue with up to 32 commands. In contrast, NVMe was designed from the ground up for flash storage and supports up to 64,000 queues, with each queue capable of handling up to 64,000 commands simultaneously. With Native NVMe in Windows Server 2025, the storage stack is purpose-built for modern hardware—eliminating translation layers and legacy constraints. Here’s what that means for you: Massive IOPS Gains: Direct, multi-queue access to NVMe devices means you can finally reach the true limits of your hardware. Lower Latency: Traditional SCSI-based stacks rely on shared locks and synchronization mechanisms in the kernel I/O path to manage resources. Native NVMe enables streamlined, lock-free I/O paths that slash round-trip times for every operation. CPU Efficiency: A leaner, optimized stack frees up compute for your workloads instead of storage overhead. Future-Ready Features: Native support for advanced NVMe capabilities like multi-queue and direct submission ensures you’re ready for next-gen storage innovation. Performance Data Using DiskSpd.exe, basic performance testing shows that with Native NVMe enabled, WS2025 systems can deliver up to ~80% more IOPS and a ~45% savings in CPU cycles per I/O on 4K random read workloads on NTFS volumes when compared to WS2022. This test ran on a host with Intel Dual Socket CPU (208 logical processors, 128GB RAM) and a Solidigm SB5PH27X038T 3.5TB NVMe device. The test can be recreated by running "diskspd.exe -b4k -r -Su -t8 -L -o32 -W10 -d30 testfile1.dat > output.dat" and modifying the parameters as desired. Results may vary. Top Use Cases: Where You’ll See the Difference Try Native NVMe on servers running your enterprise applications. These gains are not just for synthetic benchmarks—they translate directly to faster database transactions, quicker VM operations, and more responsive file and analytics workloads. SQL Server and OLTP: Shorter transaction times, higher IOPS, and lower tail latency under mixed read/write workloads. Hyper‑V and virtualization: Faster VM boot, checkpoint operations, and live migration with reduced storage contention. High‑performance file servers: Faster large‑file reads/writes and quicker metadata operations (copy, backup, restore). AI/ML and analytics: Low‑latency access to large datasets and faster ETL, shuffle, and cache/scratch I/O. How to Get Started Check your hardware: Ensure you have NVMe-capable devices that are currently using the Windows NVMe driver (StorNVMe.sys). Note that some NVMe device vendors provide their own drivers, so unless using the in-box Windows NVMe driver, you will not notice any differences. Enable Native NVMe: After applying the 2510-B Latest Cumulative Update (or most recent), add the registry key with the following PowerShell command: reg add HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Policies\Microsoft\FeatureManagement\Overrides /v 1176759950 /t REG_DWORD /d 1 /f Alternatively, use this Group Policy MSI to add the policy that controls the feature then run the local Group Policy Editor to enable the policy (found under Local Computer Policy > Computer Configuration > Administrative Templates > KB5066835 251014_21251 Feature Preview > Windows 11, version 24H2, 25H2). Once Native NVMe is enabled, open Device Manager and ensure that all attached NVMe devices are displayed under the “Storage disks” section. Monitor and Validate: Use Performance Monitor and Windows Admin Center to see the gains for yourself. Or try DiskSpd.exe yourself to measure microbenchmarks in your own environment! A quick way to measure IOPS in Performance Monitor is to set up a histogram chart and add a counter for Physical Disk>Disk Transfers/sec (where the selected instance is a drive that corresponds to one of your attached NVMe devices) then run a synthetic workload with DiskSpd. Compare the numbers before and after enabling Native NVMe to see the realized difference in your real environment! Join the Storage Revolution This is more than just a feature—it’s a new foundation for Windows Server storage, built for the future. We can’t wait for you to experience the difference. Share your feedback, ask questions, and join the conversation. Let’s build the future of high-performance Windows Server storage together. Send us your feedback or questions at nativenvme@microsoft.com! — Yash Shekar (and the Windows Server team)
