We’ve made using an AI hyperscalerTM the new gold standard for forward-thinking companies looking to push the boundaries of generative AI. If AI enterprises want to stay on the cutting edge of tech, they must trade reliance on general-purpose clouds for a partnership with a cloud provider dedicated to AI.
But what even is an AI hyperscaler? It’s an organization that operates, builds, and manages massive computing infrastructures purpose-built to power large-scale GenAI applications.
Here’s the issue: With the AI boom in full throttle, there’s a saturation of platforms calling themselves hyperscalers on the market. That influx of options can make it tough for AI enterprises to confidently make an already difficult decision: Which cloud should they use to build their projects?
At CoreWeave, we know what it takes to build an AI hyperscalerTM because we are one. We established these five features as the ultimate foundation for powering GenAI applications today and beyond.
1. Access to the latest GPUs with industry-leading performance at supercomputing scale
We know that the newest GPUs are always better. Even before its official release, it was announced that NVIDIA Blackwell architecture delivered 4x enhanced performance compared to H100 architecture. That’s why we make it a point to be first to market with the latest and greatest NVIDIA GPUs.
NVIDIA is the gold standard for GPUs that support the high-intensity compute tasks of model training and inference. In fact, A100s, H100s, and H200s are specifically optimized for AI workloads, delivering the performance teams need to build models better and faster than ever before.
*Learn how our supercomputer trained an entire GPT-3 LLM workload in under 11 minutes using 3,500 H100 GPUs—4.4x faster than the next best competitor.
Right now, we’re gearing up for Blackwell’s official release with the GB200 NVL72 rack, complete with liquid cooling capabilities, an impressive 130kW rack power, and up to 30x faster real-time trillion parameter LLM inference.
*Find out more about what we’re doing with Blackwell.
It’s one thing to give our customers access to compute—it’s another thing entirely to ensure they get the most out of it. We built multiple layers of our platform to deliver the highest possible performance out of GPUs:
- Highly performant and reliable storage. CoreWeave Storage is tailor-made for GenAI workloads. Our Object Storage offering is designed to give a more direct path between GPUs and data, making data access and transfer faster than ever. See a significant per-GPU performance boost with up to 1GB/s per GPU.
- Fast networks with ultra-low latency. We partnered with NVIDIA to build a custom large-scale network utilizing InfiniBand fibers. Our SHARP-enabled network provides the most effective bandwidth and utilizes one-to-one non-blocking channels, enabling GPUs to communicate with sub-microsecond latency.
Those critical features combined enable us to provide customers with superior cluster performance and support for massive scale. Connect 100k+ GPU superclusters to create and leverage the power of 300k+ GPU megaclusters to superpower GenAI workloads—and get to market faster than ever.
2. Reliable infrastructure and cluster health
Disruptions can be a real thorn in the side when working on model training.
Get more from your AI cluster with fewer interruptions. Reliable infrastructure is the backbone of any AI project, and it starts with hyper-efficient data center operations that enable peak cluster health.
CoreWeave Mission Control enables proactive health-checking and node lifecycle management practices to ensure high performance and nip issues in the bud. With Fleet Lifecycle Controller (FLCC), GenAI teams can rest assured that nodes are healthy throughout their entire lifecycle and continuously working in lockstep. This maximizes performance and uptime, which is absolutely essential to AI enterprises hoping to get their solutions to market quickly.
Mission Control is also supported by Node Lifecycle Controller (NLCC), which minimizes interruptions by actively monitoring nodes through proactive health checks. When any node shows signs of being unhealthy, NLCC quickly replaces it, reducing the duration, frequency, and cost of disruptions.
Cutting-edge transparency and observability enhance the benefits of FLCC and NLCC. We made sure to provide exceptional tools that measure, monitor, and diagnose node and cluster health issues. Our observability platform offers top-tier visibility into the key metrics your teams need to efficiently track node performance, pinpoint root causes of interruptions, and proactively prevent potential disruptions.
Additionally, our infrastructure enables real-time troubleshooting supported by both people and automated processes. Our FleetOps team is ready to chat 24/7, reducing downtime between job interruptions and providing a more resilient and dependable infrastructure. At the same time, our CloudOps team brings deep expertise in maintaining cloud infrastructure and operations. Our experts respond to customer needs anytime, anywhere—backed by deep expertise in cloud infrastructure that ensures seamless operations, even for the most demanding applications.
3. Transparent cluster insight
Legacy hyperscalers provide some level of visibility into cluster health and performance—but they rarely get into the nitty gritty. Here’s why that’s a problem.
You’re reliant on your vendor for fixes. When job interruptions happen, AI teams must wait for cloud providers to fix the issue instead of being empowered to address it themselves. This can cause a lot of unnecessary, preventable downtime.
You won’t know what went wrong. Because AI teams lack insight into the infrastructure status they’re building, they won’t know what exact issues cause interruptions. That makes it harder to avoid the same issues in the future.
CoreWeave Mission Control provides observability that can significantly benefit AI enterprises when executing model training and inference jobs. At CoreWeave, we aim to provide the most transparent cluster insight available on the market.
Our level of observability helps unlock ultra-fast recovery from interruptions precisely because you can see what went wrong—and then learn from it. Get insights as granular as GPU temperatures to help nip problems in the bud and prevent the same problems from happening in the future.
That means less time spent firefighting—and more time dedicated to getting models to market.
4. Performant and flexible managed software services
At CoreWeave, we know that getting the best GenAI models to market takes more than just the best hardware. You also need the best set of managed cloud services that make it easy for teams to handle workloads and get jobs up and running with high performance, flexibility, and efficiency.
That’s why we built a full suite of managed software services uniquely tailored for GenAI use cases.
CoreWeave Kubernetes Service (CKS). Spin-up GPU superclusters in the ideal environment for AI model development, featuring ultra-low latency, high-speed interconnects, and strategic automation to drive peak efficiency. Experience the benefits of Kubernetes on bare metal and unlock optimal node performance, lower latency, and faster time to market.
With CKS, you’ll get the most out of your GPUs. We offload tasks onto NVIDIA BlueField DPUs—and utilize the best suite of supporting CPUs—to get maximum performance from your GPUs during model training, experimentation, and inference.
Plus, superior Kubernetes management frees your teams from the complexities of cluster administration by incorporating AI-focused guardrails through CKS. Rely on our skilled engineers and automated processes to handle the administrative load so your developers can focus on what matters most: driving innovation.
Slurm on Kubernetes (SUNK): We developed SUNK specifically for the unique needs of GenAI when it comes to effectively utilizing compute. Run Kubernetes workloads through Slurm and colocate jobs all on the same cluster—and seamlessly share resources across workloads. That includes training, inference, experimentation, and everything else you need to build a GenAI model.
Virtual Private Cloud (VPC): Security shouldn’t have to sacrifice speed. We provide highly secure virtual networks with in-transit encryption that allow your teams to seamlessly connect compute, storage, and all essential resources for next-gen AI applications within a dedicated VPC. Plus, you’ll get support for multi-cloud strategies when you need it. CoreWeave VPCs can link to on-premises networks or traditional hyperscalers via Direct Connect, giving you flexibility in managing diverse environments.
5. Modern data centers for modern workloads
Traditionally built data centers cannot meet AI model production's growing needs and demands. AI enterprises need contemporary, modernized data centers that directly address current challenges—and plan ahead for potential roadblocks in the future.
At CoreWeave, our data centers don’t follow the “plug-in and play” model. We approach our data centers with these core tenets:
- Early design involvement
“Built from the ground up” shouldn’t just be a catchy one-liner. It should be a real, fact-backed statement. Our strong partnerships with hardware and manufacturing organizations ensure the highest possible GPU quality and performance. Plus, we utilize strategic geopositioning that keeps GenAi use cases top of mind - Built-in observability
AI-specialized clouds cannot approach data centers with a “plug in and get it online” attitude. More complex networks mean there are now hundreds of thousands of connections that could become a single point of failure. Our modern data centers have observability tools and teams in place to monitor and maintain every connection. - Liquid cooling capabilities
The more powerful the GPU, the hotter the heat generated. Air cooling alone cannot keep up with the sheer energy generated by modern processors and GPUs. Combining air cooling with liquid cooling offers a more efficient, effective, and ultimately—more future-forward—way of enabling better heat transfer and reducing energy consumption. All CoreWeave data centers will have liquid cooling capabilities by 2025.
An AI hyperscalerTM that grows with you
If legacy hyperscalers struggle to address the immediate demands of the GenAI boom, then they will certainly also struggle to address the extensive challenges that lie ahead. Challenges around scaling, power density, and capacity planning are not distant issues on the horizon—many are issues coming to a head right now.
At CoreWeave, we’ve built the real first AI hyperscalerTM. We meet GenAI’s current needs while embodying the flexibility and future-first vision promised by the current AI boom. We’re a hyperscaler that grows with you, not after you—fostering innovative and sustainable growth in a constantly evolving landscape.
CoreWeave ispurpose-built for AI, not retrofit after the fact. Get in touch to learn why we’re the AI hyperscalerTM for leading enterprises on the bleeding edge.
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