HPC in the Enterprise Back Office

Introduction

High-Performance Computing (HPC) is no longer confined to government labs and academia – it is rapidly entering the enterprise back office to power the next generation of AI-driven workloads.

Organizations across industries are investing heavily in HPC-class infrastructure to support artificial intelligence (AI) initiatives ranging from advanced research to real-time inference. In fact, enterprises are expected to triple or even quadruple their use of HPC-style compute clusters within the next few years. This convergence of HPC and enterprise IT is driven by a common goal: to extract competitive advantage from AI at unprecedented speed and scale. C-suite leaders now recognize that the massive computational power once reserved for supercomputers is becoming essential for everyday business innovation. The challenge is ensuring the enterprise has the data infrastructure to match this compute intensity – which is where DDNʼs data intelligence platform comes in.

Convergence of HPC and AI in Key Industries

What does HPC migrating into the enterprise actually look like? In many industries—including financial services, healthcare, and the public sector—HPC has long played a role in specialized use cases like Monte Carlo simulations, high-frequency trading, and genomics. Whatʼs changing now is the expansion and integration of HPC-grade tools across broader enterprise AI workflows, including sovereign AI initiatives.

Financial Services

Banks, hedge funds, and insurers are running AI models for fraud detection, algorithmic trading, and risk modeling that rival the complexity of academic supercomputing. These workloads consume massive data streams and require split-second processing. In these environments, low latency and high throughput are paramount – for example, DDNʼs platforms have helped enterprises achieve 70% faster fraud detection by eliminating data bottlenecks. Likewise, high-frequency trading operations depend on feeding AI models with data in real time without delays, a task traditionally handled by HPC infrastructure.

Healthcare and Life Sciences

From genomics sequencing to AI-assisted medical imaging, healthcare organizations are processing petabytes of unstructured data to drive research and diagnostics. These are classic HPC problems now intertwined with AI – training large bioinformatics models or running clinical “copilotˮ assistants. The payoff for HPC-level performance is huge: leading genomics labs have seen model runtimes drop by 80% after adopting HPC-optimized storage like DDN. By providing extremely high I/O bandwidth and metadata throughput, HPC-class storage enables life sciences teams to make life-changing discoveries faster.

Sovereign AI and Public Sector

Government agencies and defense organizations are building “sovereign AIˮ capabilities – AI models and infrastructure under national control – which demand HPC-like horsepower. Whether itʼs military simulation, real-time intelligence analysis, or large language models for government use, these projects require secure, compliant, and performant data infrastructure. Data sovereignty is a critical concern: losing control of sensitive data can lead to breaches and regulatory violations. Platforms like DDN ensure that data stays within national borders and remains in full compliance with local regulations, while still delivering performance at scale. This combination allows public sector innovators to pursue AI initiatives confidently, knowing their data is both fast and under control.

Other sectors echo the same theme. Media and entertainment studios leverage GPU clusters (mini render-farms) for AI in visual effects; energy companies use AI models on HPC rigs for seismic analysis and smart grid optimizations. Across the board, enterprises are discovering that advanced AI workloads come hand-in-hand with HPC-grade requirements. The convergence of HPC and AI is thus not a niche trend but a broad shift – one that blurs the line between the research supercomputer and the enterprise data center.

AI-Centric Workloads Demand HPC-Grade Infrastructure

Deploying AI at scale places extraordinary demands on infrastructure, especially in the data pipeline and storage layer.

Traditional enterprise IT solutions – designed for moderate concurrency and transaction processing – often falter when faced with AIʼs appetite for data. Key requirements include:

1. GPU-Accelerated Performance: Modern AI training and inference rely on clusters of GPUs or specialized accelerators. These chips can process data at extreme speeds, but only if they are continuously fed with data. A slow storage system will starve the GPUs, leading to wasted investments in expensive hardware. To avoid idle GPUs, enterprises need storage that delivers massive parallel throughput and sub-millisecond latency. For example, Lustre (a parallel file system born in HPC) is designed to ensure GPUs are never starved: it can feed large AI models with data at memory-speed I/O rates, minimizing idle time.
2. DDN EXAScaler®, an enterprise-ready Lustre solution, is one such system proven to deliver tens of gigabytes per second per client and keep hundreds of GPUs fully saturated. As Googleʼs recent partnership with DDN shows, the cloud is also embracing this approach – Googleʼs Managed Lustre service uses DDNʼs technology to provide 125 MB/s to 1000 MB/s per TB of throughput in the cloud, ensuring high-speed data access for AI workloads. Google selected DDNEXAScaler® to power its first party parallel file service for AI and HPC workloads.
3. High Concurrency and I/O Throughput: AI workloads like deep learning training involve thousands of parallel threads reading and writing data simultaneously (e.g. multiple GPUs each loading different data shards). Similarly, in large-scale inference or data analytics, many processes may query or update models concurrently. This high concurrency can overwhelm conventional storage or network-attached storage systems. HPC architectures solve this via parallel I/O and high-bandwidth fabrics (e.g. InfiniBand or RDMA over converged Ethernet), enabling many-to-many communications with minimal overhead. The result is that storage bottlenecks are removed, and data flows efficiently to a large number of compute nodes. Real-world AI benchmarks reflect this: the IO500 test (an industry-standard HPC storage benchmark) measures performance under mixed, metadata-intensive, concurrent workloads – exactly the kind of I/O pattern AI training exhibits. The latest IO500 results show that well-architected HPC storage can outperform conventional setups by an order of magnitude in these scenarios. In short, enterprise AI requires an HPC-like focus on eliminating I/O contention and scaling out throughput as more users or processes hit the system.
4. Metadata-Rich Data Management: AI datasets are not only large; they are also unstructured and metadata-heavy. Consider a computer vision training set with millions of small image files, each carrying annotations (labels, timestamps, source) – or an enterprise data lake where every object might have tags and compliance flags. Metadata adds enormous value by making data searchable and enabling dynamic AI workflows, but it also creates new challenges for the storage layer. Many legacy systems struggle when asked to handle millions of files or billions of objects with rich metadata – the metadata operations can overwhelm the controllers and degrade performance. Modern AI infrastructure often embraces object storage for its scalable metadata capabilities (e.g. tagging, search). However, raw object stores can be too slow for active AI training unless accelerated. This is why solutions like DDNʼs Infinia were developed – bringing an object storage paradigm with HPC performance. Infiniaʼs architecture uses a distributed metadata engine capable of tens of thousands of tags per object, so AI teams can organize and label data at will without bottlenecking the system. By combining a parallel file core with an object interface, such platforms give the best of both worlds: rapid data access alongside rich metadata features for AI data preparation.
5. Hybrid and Multi-Cloud Workflows: Enterprises are increasingly deploying AI across a mix of environments – on-premises data centers, public clouds, and edge locations – forming hybrid or multi-cloud pipelines. A model might be trained on an on-prem HPC cluster, then deployed to the cloud for inference at scale; or data might be collected at the edge and sent back to a core data center for AI analysis. These distributed workflows demand infrastructure that can seamlessly move and synchronize data between environments. Without careful planning, data silos and inconsistent formats can make AI adoption unmanageable. HPC systems historically werenʼt built with cloud interoperability in mind, but this is changing fast. Enterprise AI storage must support cloud bursting, caching, and replication across sites. DDNʼs solutions, for instance, enable a unified data view across core, cloud, and edge – so that an AI pipeline can span multiple locations without painful data migrations. As an example, DDN Infinia can act as an on-prem object store and be instantiated in Google Cloud, providing a consistent S3 interface and policy-based data movement between on-prem and cloud. The ability to maintain high performance while shuttling data between on-prem GPUs and cloud tools is becoming a key requirement for enterprise AI infrastructure.
6. Governance, Security, and Compliance: Finally, as AI matures in the enterprise, governance is as critical as performance. Companies must ensure that AI data – which might include sensitive customer information or intellectual property – is handled with strict security and compliance controls. This is a new twist for HPC-grade systems, which were traditionally optimized for speed in trusted environments. In the enterprise back office, however, features like encryption, audit logging, access controls, and data lineage tracking are mandatory. AI regulations (such as emerging AI ethics guidelines or industry-specific rules) are on the horizon, so infrastructure must be ready to support governance at scale. Modern AI data platforms therefore build in these capabilities: for example, DDNʼs Infinia integrates fine-grained data tagging, policy automation, and lineage tracking to help enterprises maintain compliance and trust in AI workflows. Zero-trust security models (ensuring every access is verified) and multi-tenant isolation (so different teams or customers sharing infrastructure remain walled off) are also essential. Enterprise-grade HPC solutions now offer these features – DDNʼs appliances, for instance, provide multi-tenant QoS and per-tenant encryption/isolation to meet enterprise security needs. Simply put, as AI moves from experimentation to production, the underlying infrastructure must not only be fast but secure and accountable.

Taken together, these requirements illustrate why a typical cloud storage bucket, or a traditional NAS is often insufficient for serious AI initiatives. Enterprise AI is pushing into the realm of HPC – requiring the extreme performance of parallel file systems and GPU-optimized pipelines, combined with the manageability and governance features of enterprise IT. This is the gap that vendors like DDN are aiming to fill, by bringing HPC technologies like Lustre into enterprise-friendly solutions and fusing them with advanced data management.

Bridging the Gap: DDNʼs Data Platforms for AI and HPC

As enterprises look to marry HPC capabilities with business needs, DDN has emerged as a key enabler.

DDN made its name over the past two decades powering storage at the worldʼs fastest supercomputers – from national labs to industry research centers. Leveraging that pedigree, DDN has become a leader in AI-focused data infrastructure, effectively bridging its HPC expertise into enterprise AI solutions. A recent $300M strategic investment by Blackstone values DDN at $5B and underscores this mission of translating supercomputing know-how into AI-driven enterprise offerings.

What makes DDN uniquely suited to this convergence? In essence, its technology stack combines HPC-class performance with enterprise-grade features:

1. EXAScaler® (Parallel File System): At the core of DDNʼs portfolio is the EXAScaler® appliance line, which runs the Lustre parallel file system. Lustre is an open-source technology born in HPC, known for its ability to deliver extremely high throughput by stripping data across many servers and disks in parallel. DDN has productized and enhanced Lustre for enterprise use via EXAScaler® – offering turnkey storage appliances (such as the AI400X series) that can feed data-hungry GPU clusters. Each DDN AI400X node can sustain up to 110 GB/s of throughput and 4 million IOPS from a single 2U system, an astounding performance level that ensures even the largest AI models (think multi-billion parameter training runs) are not I/O-bound. These performance claims are borne out by industry benchmarks: DDN consistently dominates the IO500 results, outperforming other HPC storage vendors by up to 11× in mixed AI/HPC workload tests. Crucially, DDN has adapted its HPC filesystem for enterprise reliability and ease of use – adding features like data compression, snapshots, and non-disruptive upgrades. Unlike traditional HPC deployments that might tolerate some downtime for manual tuning, enterprise customers demand continuous availability. DDNʼs latest EXAScaler® appliances allow hardware or software components to be replaced without stopping the array, minimizing downtime to near zero. This kind of “five ninesˮ resilience, combined with maintenance automation, makes HPC storage viable in mission-critical back-office environments where every minute of uptime counts.
2. Infinia® (AI Data Intelligence Platform): The second pillar of DDNʼs solution is Infinia, a next-generation data management platform built to address AI-specific needs around unstructured data and metadata. Infinia is essentially an AI-optimized object storage with an integrated metadata engine and rich data services. Where EXAScaler® provides raw performance to feed GPUs, Infinia focuses on the “intelligenceˮ of the data – allowing enterprises to tag, search, and orchestrate data seamlessly across hybrid cloud environments. This product was introduced as DDN recognized that enterprise AI workflows increasingly gravitate toward object storage for its flexibility (cloud-native access via S3 API and limitless scaling). However, standard object stores werenʼt built for AIʼs performance needs, so Infinia was engineered to be 100× faster in certain metadata operations and 10× lower latency than public cloud storage alternatives. Infinia 2.2 (the latest release) brings features like an S3-compatible Hadoop/Spark connector for high-performance analytics, native integration with monitoring tools (Datadog, OpenTelemetry) for deep observability, and the ability to tier data between core and cloud dynamically. With Infinia, enterprises can unify their AI data across on-prem and cloud, apply tens of thousands of tags per object for fine-grained categorization, and enforce policies (like auto-archiving cold data or replicating critical datasets off-site) all through one interface. It essentially adds a “brainˮ on top of high-speed storage, automating data management tasks that would otherwise bog down AI projects.
3. Enterprise Feature Set: Beyond raw performance and data intelligence, DDN has layered important enterprise features onto its platform. Multi-tenancy is a prime example. In large enterprises or service providers, one storage system may be shared by many teams or departments running different AI projects. DDN supports secure multi-tenancy with per-tenant network isolation (VLANs), quotas, and role-based access controls. This ensures that one groupʼs AI workload cannot interfere with anotherʼs, and that sensitive data is isolated as needed. DDN also emphasizes ecosystem integration – its storage is certified or validated with all major AI frameworks and hardware. There are reference architectures with NVIDIAʼs DGX supercomputers, support for GPU Direct Storage, and plugins for AI workflow managers. Such integrations make it easier for enterprise IT teams to deploy DDN in existing environments without disruption. Finally, DDNʼs long experience in HPC means it has a proven track record at extreme scale: it has powered deployments with hundreds of petabytes and tens of thousands of clients, something few competitors can claim. As Paul Bloch, DDNʼs co-founder, noted, feeding thousands of GPUs concurrently requires storage throughput in the tens of terabytes per second – a feat that only a handful of systems on the planet can achieve, and DDN has mastered it over years of honing HPC solutions.

In summary, DDN serves as the bridge between HPC and enterprise AI by offering a platform that doesnʼt force a trade-off between performance and manageability. It brings the “muscleˮ of HPC – fast parallel file I/O, robust throughput, scalability – together with the “mindˮ of enterprise IT – data intelligence, security, and cloud integration. This makes DDNʼs solutions particularly well-suited to organizations that are serious about operationalizing AI and need their infrastructure to keep up with their ambitions.

Optimal Use Cases for DDNʼs AI Infrastructure

Not every AI workload demands an HPC-grade solution, but for many cutting-edge applications, DDN provides clear advantages.

Below is a summary of ideal scenarios where DDN excels, and how its strengths map to each use case:

In these scenarios, DDNʼs unique combination of performance and unified data management provides a competitive edge. For instance, an AI model training pipeline might involve reading millions of small image files per second – a task where DDNʼs parallel file system dramatically outperforms a conventional NAS, delivering the required IOPS and throughput. In a genomics workflow, the ability of Infinia to tag and organize data (patient records, experiment metadata) while streaming data at speed helps maintain compliance (HIPAA, for example) and accelerate research. Financial institutions running risk models benefit from DDNʼs low-latency design – when thousands of model inference requests hit the storage simultaneously, the system can handle the concurrent I/O without queueing delays, ensuring timely results for decision-makers.

When to Use DDN (and When Not To)

Itʼs important to note that DDNʼs solutions are purpose-built for scale and performance.

They shine in complex, demanding AI environments, but might be overkill for simpler needs. Executives should consider DDN when their AI workloads hit one or more of the following thresholds:

1. Extreme Performance Requirements: You are training on GPU clusters (NVIDIA DGX or similar) or running HPC simulations where storage must deliver tens of GB/s and millions of IOPS. DDN is specifically optimized for these cases, with parallel I/O and GPU integration. If your GPUs are sitting idle waiting for data, thatʼs a clear sign to evaluate a solution like DDN that can feed them faster.
2. Massive Data Scales (Petabyte to Exabyte): Your data volumes are in the petabyte range and growing fast, such as exascale scientific datasets or global enterprise data lakes. DDN has proven deployments at exascale – for example, in national labs and research institutions – and its architecture is designed to scale linearly to billions of files. Traditional enterprise storage often breaks down at this scale (performance or management-wise), whereas DDN was built for it.
3. AI Data Governance & Analytics: You require fine-grained control over data, such as tracking lineage of training data, enforcing retention policies, or performing analytics on metadata. DDNʼs Infinia is unique in offering integrated data intelligence capabilities (tagging, search, policy automation) on top of high-performance storage. If compliance, auditability, or complex data workflows are top concerns, DDN provides an all-in-one solution where others would require bolting on separate tools.
4. Multi-Tenant AI Infrastructure: If your organization is consolidating AI efforts into a shared “AI factoryˮ or platform (common in large enterprises, service providers, or research hubs), DDNʼs multi-tenant features ensure each user or team gets reliable performance and security isolation. This is ideal for enterprise AI labs or innovation centers where many projects run on the same core infrastructure.
5. Hybrid Cloud or Edge Integration: When your AI pipeline spans on-prem and cloud, or core data center and edge locations, DDN can enable a seamless hybrid architecture. Its ability to replicate and sync data across environments (with Infinia and EXAScaler® Cloud) means you can burst to cloud for compute or aggregate edge data back centrally without retooling your applications. Organizations pursuing a cloud-augmented AI strategy – moving data where itʼs needed on the fly – should consider this capability.
6. Regulated or Security-Sensitive Environments: In industries like healthcare, finance, government, or energy, where compliance standards are strict (HIPAA, GDPR, FedRAMP, etc.), DDN brings proven trust. Its systems are used in highly secure environments (defense, intelligence, etc.) and support encryption, secure multi-tenancy, and auditing features that help meet regulatory requirements. If failing an audit or a security breach is simply not an option, enterprise leaders often turn to the hardened solutions DDN provides.

Conversely, if an organizationʼs AI needs are relatively modest – for example, a small startup experimenting with <100 TB of data, or an analytics workload that is mostly handled by cloud-native services – a lightweight cloud storage or basic NAS might suffice. DDN is not aimed at “small dataˮ or low IOPS scenarios. Cases where DDN may not be the best fit include:

• Lightweight, Cloud-Native AI Apps: If your AI application is simple, cloud-based, and doesnʼt require high throughput or fine-grained data control (e.g. an AI prototype using a few cloud VMs and an object store), deploying a high-end storage solution could be unnecessary. DDNʼs value lies in scale and speed; small-scale workloads that can run on standard cloud storage wonʼt justify the investment.
• Budget-Constrained or Simplicity-Prioritized Teams: Organizations early in their AI journey—with limited IT resources or budget—may prioritize simplicity over peak performance. For small teams (e.g., a 5-person data science group), fully managed cloud platforms like Google Managed Lustre, EXA Cloud, or Infinia on Google or OCI can offer a faster, easier on-ramp. While DDN solutions deliver unmatched performance at scale, they do require more operational maturity than plug-and-play cloud services. As needs grow—larger datasets, shared infrastructure, greater cost sensitivity—DDNʼs value and TCO advantages become significantly more compelling.

In short, DDN should be viewed as a strategic enabler for AI at scale. When an enterprise reaches the point where AI is core to operations – and slow model training or data bottlenecks translate to lost opportunities – that is when DDNʼs HPC-derived capabilities become indispensable. If you are not at that point yet, cloud-native tools might bridge the gap for the time being. The table above and criteria here can guide decision-makers on when the tipping point is reached.

The Road Ahead: AI at Scale Needs HPC-Caliber Solutions

Looking forward, the trend of HPC migrating into the enterprise back office is set to accelerate.

AI itself is evolving – moving from isolated pilot projects to agentic, autonomous AI systems that will pervade business processes. These next-generation workloads (e.g. AI copilots that assist in every department, or autonomous agents that make real-time decisions in finance, healthcare, etc.) will raise the bar even higher for infrastructure in several ways:

1. Always-On, Real-Time Processing: Future AI “agentsˮ are expected to operate continuously and interact with users or data streams in real time. This means infrastructure must deliver split-second response times consistently. For example, an agentic AI system in healthcare might monitor patient vitals and adjust treatment plans on the fly – a delay in retrieving data or model output could be life critical. HPC-grade low latency (sub-millisecond access) will be mandatory. As DDN notes, agentic AI “demands lightning-fast, scalable infrastructureˮ capable of feeding intelligent agents with real-time context as fast as they can think. In practical terms, that means widespread adoption of technologies like NVMe-over-fabrics, GPU Direct Storage, and distributed in-memory caches – all areas where HPC architectures excel and are being productized for enterprise use.
2. Distributed (Edge-to-Cloud) Footprint: AI will not live solely in big data centers; it will be distributed across edge devices, branch offices, and multi-cloud deployments. Agentic AI, for instance, might involve an edge component (like a factory robot or an autonomous vehicle) that constantly syncs with a central brain in the cloud. Managing data coherence and orchestration across core, cloud, and edge will be a major challenge. The infrastructure must ensure that an AI agent can access the latest relevant data regardless of location, without expensive duplicate storage or transfer delays. This is essentially an HPC problem of distributed computing, now applied to enterprise scale. DDNʼs vision already hints at this: their platform is designed so that agents “stay in sync – moving seamlessly between environments with no need for data duplication or pipeline rewritesˮ. We can expect enterprise IT to increasingly adopt such intelligent data fabrics (much like HPC distributed file systems, but spanning edge to cloud) to enable fluid AI operations everywhere the business needs them.
3. Integrated Governance and Trust: As AI systems become more autonomous (so-called agentic AI), organizations and regulators will insist on greater oversight. Questions of data provenance, model bias, security, and compliance will take center stage. An autonomous financial trading agent, for example, must be tightly governed to avoid rogue behavior and to audit its decisions against regulatory standards. This implies that governance must be backed into the infrastructure – not added as an afterthought. HPC infrastructure in the enterprise will thus incorporate advanced identity and access management, encryption of data in transit and at rest, continuous auditing, and perhaps even real-time policy enforcement driven by AI itself. DDNʼs platform already emphasizes “built-in data governance for trusted autonomy,ˮ with capabilities like zero-trust access controls and privacy-preserving workflows at scale. Going forward, the winners in AI infrastructure will be those who can offer both performance and strong governance. AI-assisted decision-making will only be as good as the reliability and integrity of the data itʼs based on.
4. Scale and Resilience: The scale of AI deployments is poised to grow exponentially. Consider that one leading AI company recently deployed 200,000 GPUs in a cluster and plans to grow to 1 million GPUs for AI work – numbers that were unimaginable for a private enterprise just a few years ago. Even more moderate firms will see their AI user base and model complexity grow by order of magnitude as AI is embedded in products and services. This means infrastructure must not only scale in performance but also be operable at scale. Automation in management, self-healing systems, and AI-driven optimization of resources will be critical to handle clusters of such size within enterprise IT departments. HPC technologies like DDNʼs – which already support massive scales – will serve as a foundation. Furthermore, as AI becomes mission-critical, downtime is unacceptable. Systems must be resilient (fault-tolerant hardware, georedundancy for disaster recovery) so that AI services are always available. Enterprise back offices may need to adopt the kind of high-availability designs seen in HPC mission environments (e.g. failover protocols, redundant networks) to meet these expectations.

In essence, the future of enterprise AI is one that fully merges with HPC: the performance, concurrency, and scale long associated with supercomputers will be required ubiquitously, inside businesses of all kinds. As that happens, a platform like DDNʼs becomes not just advantageous but necessary to sustain AI-driven operations. In fact, industry experts already advise that most IT infrastructure will need a full overhaul – storage, compute, and networking – to cope with AI, otherwise organizations risk stalling their AI initiatives. The cost of insufficient infrastructure isnʼt just technical – itʼs business performance. Every minute of a stalled AI job is time competitors could use to leap ahead.

For the C-suite, the key takeaway is that AI at scale is a data problem as much as a compute problem. Investing in accelerated computing (GPUs, TPUs, etc.) must go hand-in-hand with investing in an HPC-grade data infrastructure that can fuel those chips with timely, governed, and scalable data.

Conclusion

The convergence of HPC and enterprise back-office operations is redefining what “IT – infrastructureˮ means for the AI era.

Leaders in finance, healthcare, government, and beyond are realizing that to leverage AI – whether for insightful predictions, intelligent automation, or autonomous agents – they must embrace technologies historically forged in supercomputing. This shift is about adopting HPC-level performance and scalability in everyday business IT, while also upholding the manageability and governance that enterprises require.

DDN exemplifies how this convergence can be achieved. By bringing together a parallel file system that can feed AI models at record speed and an intelligent data platform that simplifies and secures data across environments, DDN provides a critical missing piece for AI-focused enterprises. It allows organizations to unlock AI innovation at scale without breaking the back of their infrastructure teams or compromising on compliance. In promotional terms, DDN serves as the backbone for turning lofty AI strategies into practical, high-impact outcomes – from speeding up R&D breakthroughs to ensuring customer-facing AI services run without a hiccup.

For executives plotting their companyʼs AI roadmap, the message is clear: to get the most out of AI investments, you must treat data infrastructure as a strategic priority. High-performance computing is no longer an adjunct or a luxury; itʼs becoming the engine of enterprise AI. Solutions like DDNʼs are emerging as the bridge to this new reality – enabling enterprises to deploy AI with the confidence that their infrastructure can scale, perform, and govern at the level that these transformative workloads demand. In the coming years, the enterprises that successfully marry HPC power with AI ambition will be the ones leading their industries, powered by insights and capabilities their competitors simply canʼt catch up to. The tools are available today – and as weʼve explored, they are battle-tested and ready to bring the best of HPC into your back office, turning your AI aspirations into a sustainable competitive advantage.