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Qumulo: The industry’s fastest cloud-based file solution for AI Workloads

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The world of artificial intelligence (AI) has experienced exponential growth and has an insatiable hunger to consume data. From self-driving cars to chatbots that mimic human conversation, AI is revolutionizing industries at a blistering pace. AI’s power is derived from its data backbone. Access to data, the speed of its processing, and scalable storage performance are all pivotal factors that determine the efficiency of an AI pipeline. This is where Qumulo has proved itself to be the best data storage solution on the planet for AI workloads, as the industry’s highest performance and most cost-effective file data solution in the cloud.

Why Qumulo is Ideal for File-based AI Workloads

AI applications, be it deep learning models or neural networks, require a unique set of storage characteristics, all of which are satisfied by Qumulo:

  1. Scalability: AI datasets are dynamic. They grow over time as more data is collected and processed. Qumulo’s ability to scale with predictable high performance ensures that as AI workloads grow, Qumulo can meet its demands at any scale.
  2. Cost-effectiveness: Funding AI initiatives can be a significant investment. Saving on storage costs without compromising on performance can free up resources for other critical areas, be it research, development, or production deployments.
  3. Ability to Scale AnywhereTM: Infrastructure owners and data scientists benefit from the flexibility of training in one location, but deploying in another, with highly secure infrastructure. Qumulo’s software-defined storage system can be deployed and run anywhere. This makes it easy to train an AI model in the core data center, but push to production anywhere.
  4. Performance: AI models, especially those used in scenarios like autonomous vehicles or financial transactions, need real-time data access for pre and post model training. Qumulo’s high-speed data retrieval ensures that data is available the moment it’s required.

Let’s drill into bullet #4 and underscore the importance of seamless and lightning-fast retrieval of data / metadata. This is vital for AI applications that require scalable file storage – on premises or in the cloud.

Through testing synthetic AI workloads, we found that we are indeed the fastest file-based cloud solution on the market for AI, where data scientists can use Qumulo for data collection, pre-training, production training, and ongoing inference — no matter the scale.

Read on.

Widely Applicable AI Benchmark

To put Qumulo’s performance capabilities into perspective, let’s dive into the latest result achieved with Qumulo running in the cloud on AWS infrastructure. We used SPECstorage to characterize AI performance on Qumulo. This benchmark (aptly named AI_Image) leverages file sizes and I/O patterns that synthetically and accurately exercise common AI workloads:

  • Based on Tensorflow best practices – world’s most widely deployed AI/ML framework
  • Traced from 3 different models: Resnet, VGG (Visual Geometry Group), and SSD (Single shot detector)
  • Using open source datasets from CityScape, ImageNet, and COCO

Due to Tensorflow’s ubiquity in the AI space, the benchmark applies to a wide range of AI model workloads that produce AI results for:

  1. Image Classification and Object Detection
  2. Natural Language Processing (NLP)
  3. Speech Recognition
  4. Recommendation Systems
  5. Generative Models
  6. Healthcare and Life Sciences

…and MANY more

Benchmark Description and Results Achieved

The goal of the benchmark is to serve data quickly from Qumulo storage to the application layer (using GPUs) running the AI jobs. The benchmark tests the storage performance and latency from a realistic set of I/O patterns from a batch of clients. The clients incrementally increase their number of AI jobs until they reach the target, which in the case of this test is a total of 480 jobs. There are four main operations in the benchmark, with 4 independent concurrent sub-workloads:

  • AI_SF – Reads of small image files
  • AI_TF – Writes out larger files (ideally 100 MB+ files)
  • AI_TR – Reads in large TFRecords
  • AI_CP – Performs occasional checkpointing


Fig. 1 below displays the following results:

  • X axis shows the number of jobs running the AI benchmark over time
  • Y axis shows the overall latency during the length of the test
  • Latency shows the storage performance fast and predictable as the number of AI jobs increase!

Fig. 1

Qumulo is the faster storage for AI in the cloud

** Comparison based on best performing systems in Public Clouds published at as of October 2023. SPEC® and the benchmark name SPECgeneric® are registered trademarks of the Standard Performance Evaluation Corporation. For more information about SPECstorage2020, see

On Premises Applicability

While the SPECstorage benchmark tests used a cloud-based environment, these results can be easily extrapolated to estimate results using similar on-premises hardware. When Qumulo publishes this benchmark into SPEC’s website (ETA December/2023), the details and cost of the environment can be found, noting the EC2 instance types (number of cores, available memory,etc.) used and the bandwidth available in the network environment. In the meantime, we include the supplementary details in the appendix of this blog for curious readers.

Data Scientists and Data Engineers, Behold. Try it Yourself!

In the rapidly advancing world of AI, having a robust, fast, and scalable storage solution is not a luxury but a necessity. Qumulo, with its industry-leading performance and cost-effectiveness, stands out as the go-to cloud-based file solution for AI workloads. The benchmark not only underscores Qumulo’s prowess but also cements its position as the fastest and most widely applicable storage solution for AI.

See full results published on



Overall response time= 1.22 msec

16 1.360 6960 1565
32 1.281 13921 3127
48 1.313 20882 4691
64 1.213 27843 6255
80 1.201 34804 7822
96 1.147 41765 9385
112 1.158 48726 10950
128 1.123 55687 12514
144 1.122 62648 14082
160 1.109 69609 15644
176 1.144 76570 17208
192 1.117 83530 18774
208 1.112 90491 20340
224 1.112 97452 21899
240 1.121 104413 23470
256 1.271 111374 25037
272 1.143 118335 26598
288 1.155 125296 28161
304 1.197 132257 29729
320 1.205 139218 31289
336 1.257 146178 32859
352 1.323 153139 34418
368 1.430 160100 35984
384 1.503 167061 37552
400 1.632 174022 39112

Product and Test Information

Qumulo – Public Cloud Reference
Tested by Qumulo, Inc.
Hardware Available November 2023
Software Available November 2023
Date Tested November 2023
License Number 6738
Licensee Locations Seattle, WA USA

Qumulo is a hybrid cloud file storage solution that boasts exabyte-plus scalability in a single namespace, identical features whether on-prem or in the cloud, and full multi-protocol support, ensuring flexibility and compatibility across diverse applications. By seamlessly integrating with public cloud infrastructure, Qumulo delivers unstructured data storage at any scale, with real-time visibility into storage performance and data usage.

Qumulo’s cloud-native file system empowers organisations to seamlessly migrate file-based applications and workloads to the public cloud environment. With Qumulo, businesses can efficiently manage exabytes of data, whether on-premises or in the cloud. The following findings clearly demonstrate that the Qumulo filesystem excels in delivering outstanding performance when deployed on AWS.

Solution Under Test Bill of Materials

Item No Qty Type Vendor Model/Name Description
1 16 AWS EC2 Instances AWS c5n.18xlarge Qumulo nodes – Amazon c5n EC2 instances (c5n.18xlarge instances have 72 vCPU, 192GiB of memory, 100Gbps networking)
2 16 AWS EC2 Instances AWS c5n.18xlarge Ubuntu Clients – Qumulo cluster – Amazon c5n EC2 instances (c5n.18xlarge instances have 72 vCPU, 192GiB of memory, 100Gbps networking)

Configuration Diagrams

Qumulo in AWS

Qumulo in AWS

Component Software

Item No Component Type Name and Version Description
1 Qumulo Core File System 6.2.2 Qumulo’s cloud-native file system allows organisations to effortlessly move file-based applications and workloads to the public cloud.
2 Ubuntu Operating System 22.04 The Ubuntu operating system is deployed on the sixteen c5n.18xlarge compute nodes. They are used as clients running the SPEC Storage 2020 benchmarks.

Hardware Configuration and Tuning – Physical

Component Name
Parameter Name Value Description
SR-IOV Enabled Enables CPU virtualization technology
Port Speed 100 GbE Each node has 100 GbE connectivity

Hardware Configuration and Tuning Notes


Software Configuration and Tuning – Virtual

Parameter Name Value Description
Jumbo Frames 9001 Enables Ethernet jumbo frames up to 9001 bytes
Ubuntu Clients NFS Mount Parameters
Parameter Name Value Description
vers 3 Use NFSv3
nconnect 16 Increase the number of NFS client connections upto 16
tcp TCP network transport protocol to communicate with the Qumulo cluster
local_lock all The client assumes that both flock and POSIX locks are local
EBS Volume Parameter
Parameter Name Value Description
IOPS 16000 Max IOPS for EBS Volume
Throughput 1000 Max Throughput for EBS Volume

Software Configuration and Tuning Notes


Service SLA Notes

AWS uses commercially reasonable efforts to make the Included Products and Services each available with a Monthly Uptime Percentage of at least 99.99%, in each case during any monthly billing cycle. The Monthly Uptime Percentage is calculated by subtracting from 100% the percentage of minutes during the month in which any of the Included Products and Services, as applicable, was in the state of “Region Unavailable”.

Storage and Filesystems

Item No Description Data Protection Stable Storage Qty
1 Elastic Block Storage volume, capacity of 1TB gp3. Each Qumulo node has 6 EBS volume. 2 Drive or 1 Node Protection with Erasure Coding AWS EBS 96
Number of Filesystems 1
Total Capacity 78.54 TB
Filesystem Type Qumulo

Filesystem Creation Notes

The Qumulo Core filesystem is deployed on AWS through either a cloud formation template or Terraform. The Qumulo Core AMI is deployed and the file system is configured as a part of either the automated cloud formation process or via Terraform. No additional file system creation steps are required.

Storage and Filesystem Notes


Transport Configuration – Virtual

Item No Transport Type Number of Ports Used Notes
1 100Gbps Ethernet Virtual NIC 16 Used by client machines
2 100Gbps Ethernet Virtual NIC 16 Used by Qumulo Core for inter-nodal communications as well as communications to any clients.

Transport Configuration Notes


Switches – Virtual

Item No Switch Name Switch Type Total Port Count Used Port Count Notes
1 AWS 100Gbps Ethernet with Enhanced Networking 16 16 Used by client machines
2 AWS 100Gbps Ethernet with Enhanced Networking 16 16 Used by Qumulo Core nodes

Processing Elements – Virtual

Item No Qty Type Location Description Processing Function
1 1152 vCPU c5n.18xlarge Qumulo Core 3.5 GHz intel Xeon Platinum processors Qumulo Core, Network communication, Storage Functions
2 1152 vCPU c5n.18xlarge Qumulo Core 3.5 GHz intel Xeon Platinum processors Spec Storage Client Benchmark processors

Processing Element Notes


Memory – Virtual

Description Size in GiB Number of Instances Nonvolatile Total GiB
AWS EC2 c5n.18xlarge instance memory 192 16 V 3072
AWS EC2 c5n.18xlarge instance memory 192 16 V 3072
Grand Total Memory Gibibytes 6144

Memory Notes


Stable Storage

Qumulo Core utilises Elastic Block Storage (EBS) devices; which provide stable storage.

Solution Under Test Configuration Notes

The solution under test was a standard distributed cluster built utilising Qumulo Core. Qumulo Core clusters can handle large and small file I/O along with metadata-intensive applications. No specialised tuning is required for different or mixed-use workloads.

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