The NVIDIA L40S is a powerful multitasking GPU built for high-end computing needs.It handles demanding workloads such as AI training and professional-grade rendering with ease.Enterprises prefer L40S for its strong performance and large memory without extreme costs.Powered by the Ada Lovelace architecture, it delivers speed for modern digital workloads.This blog explains why L40S is a top choice for data centers and professional creators.
Choosing the right graphics card is a critical decision for studios and engineers.Power-intensive tasks like 3D rendering and AI training demand high-performance GPUs.Professionals often compare NVIDIA’s top options to match their budget and project needs.
This blog compares NVIDIA RTX 5000 vs RTX 6000 in terms of performance and value.It helps you decide which GPU best fits your professional workload.
Did you know? In 2026, moving to the cloud isn’t enough. As AI workloads scale, smarter optimization, cost control, and performance matter more than ever. Multi-cloud and AI-driven monitoring are now essential to stay competitive.
Fortinet Unveils Secure AI Data Center Solution for Scalable Protection
Fortinet has launched its Secure AI Data Center solution, an end-to-end framework designed to protect AI infrastructure—from GPU clusters and large-language models to data pipelines and applications—while delivering high throughput and energy efficiency for hyperscale environments.
Vertiv Teams Up with NVIDIA to Develop 800V AI Power Platforms
Vertiv has reached a significant milestone in its collaboration with NVIDIA, advancing its 800V direct current (VDC) power architecture designs to engineering readiness. This development supports NVIDIA’s next-generation AI infrastructure, including the upcoming Rubin Ultra platforms, scheduled for release in 2027.
“Larger AI workloads are reshaping every aspect of data center design,” said Scott Armul, executive vice president, global portfolio and business units at Vertiv. “Our systems-level expertise in both AC and DC-based power data center architectures positions us uniquely to address the unprecedented power demands of AI workloads. With the development of our Vertiv 800 VDC platform designs, we’re translating our extensive experience into next-generation solutions that will support the massive compute densities required for AI factories.”
Businesses process enormous datasets, execute intricate simulations, and train generative models with billions of parameters using artificial intelligence (AI) and high-performance computing (HPC) applications for a variety of use cases, including LLMs, genomic analysis, quantitative analysis, and real-time sports analytics. Their storage systems are under a lot of performance pressure from these workloads, which necessitate high throughput and scalable I/O performance that keeps latencies under a millisecond even when thousands of clients are reading and writing the same shared file at the same time.
Google Cloud is thrilled to share that Parallelstore, which is unveiled at Google Cloud Next 2024, is now widely available to power these next-generation AI and HPC workloads. Parallelstore, which is based on the Distributed Asynchronous Object Storage (DAOS) architecture, combines a key-value architecture with completely distributed metadata to provide high throughput and IOPS.
Continue reading to find out how Google Parallelstore meets the demands of demanding AI and HPC workloads by enabling you to provision Google Kubernetes Engine and Compute Engine resources, optimize goodput and GPU/TPU utilization, and move data in and out of Parallelstore programmatically.
It employs a key-value store architecture along with a distributed metadata management system to get beyond the performance constraints of conventional parallel file systems. Due to its high-throughput parallel data access, it may overwhelm each computing client’s network capacity while reducing latency and I/O constraints. Optimizing the expenses of AI workloads is dependent on maximizing good output to GPUs and TPUs, which is achieved through efficient data transmission. Google Cloud may also meet the needs of modest-to-massive AI and HPC workloads by continuously granting read/write access to thousands of virtual machines, graphics processing units, and TPUs.
The largest Parallelstore deployment of 100 TiB yields throughput scaling to around 115 GiB/s, with a low latency of ~0.3 ms, 3 million read IOPS, and 1 million write IOPS. This indicates that a large number of clients can benefit from random, dispersed access to small files on Parallelstore. According to Google Cloud benchmarks, Parallelstore’s performance with tiny files and metadata operations allows for up to 3.7x higher training throughput and 3.9x faster training timeframes for AI use cases when compared to native ML framework data loaders.
For data preparation or preservation, cloud storage is used by many AI and HPC applications. You may automate the transfer of the data you want to import into Parallelstore for processing by using the integrated import/export API. With the help of the API, you may ingest enormous datasets into Parallelstore from Cloud Storage at a rate of about 20 GB per second for files bigger than 32 MB and about 5,000 files per second for smaller files.
gcloud alpha parallelstore instances import-data $INSTANCE_ID
–location=$LOCATION –source-gcs-bucket-uri=gs://$BUCKET_NAME
[–destination-parallelstore-path=”/”] –project= $PROJECT_ID
You can programmatically export results from an AI training task or HPC workload to Cloud Storage for additional evaluation or longer-term storage. Moreover, data pipelines can be streamlined and manual involvement reduced by using the API to automate data transfers.
gcloud alpha parallelstore instances export-data $INSTANCE_ID –location=$LOCATION –destination-gcs-bucket-uri=gs://$BUCKET_NAME
[–source-parallelstore-path=”/”]
The Parallelstores GKE CSI driver makes it simple to effectively manage high-performance storage for containerized workloads. Using well-known Kubernetes APIs, you may access pre-existing Parallelstore instances in Kubernetes workloads or dynamically provision and manage Parallelstore file systems as persistent volumes within your GKE clusters. This frees you up to concentrate on resource optimization and TCO reduction rather than having to learn and maintain a different storage system.
ApiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: parallelstore-class
provisioner: parallelstore.csi.storage.gke.io
volumeBindingMode: Immediate
reclaimPolicy: Delete
allowedTopologies:
The fully managed GKE Volume Populator, which automates the preloading of data from Cloud Storage straight into Parallelstore during the PersistentVolumeClaim provisioning process, will be available to preload data from Cloud Storage in the upcoming months. This helps guarantee that your training data is easily accessible, allowing you to maximize GPU and TPU use and reduce idle compute-resource time.
With the Cluster Toolkit’s assistance, deploying Parallelstore instances for Compute Engine is simple. Cluster Toolkit is open-source software for delivering AI and HPC workloads; it was formerly known as Cloud HPC Toolkit. Using best practices, Cluster Toolkit allocates computing, network, and storage resources for your cluster or workload. With just four lines of code, you can integrate the Parallelstore module into your blueprint and begin using Cluster Toolkit right away. For your convenience, we’ve also included starter blueprints. Apart from the Cluster Toolkit, Parallelstore may also be deployed using Terraform templates, which minimize human overhead and support operations and provisioning processes through code.
Leading sports video analytics company Respo. Vision is using it to speed up its real-time system’s transition from 4K to 8K video. Coaches, scouts, and fans can receive relevant information by having Respo.vision help gather and label granular data markers utilizing Parallelstore as the transport layer. Respo.vision was able to maintain low computation latency while managing spikes in high-performance video processing without having to make costly infrastructure upgrades because to Parallelstore.
The use of AI and HPC is expanding quickly. The storage solution you need to maintain the demanding GPU/TPUs and workloads is Parallelstore, with its novel architecture, performance, and integration with Cloud Storage, GKE, and Compute Engine.
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