As enterprises transition from ideation to implementation in their artificial intelligence (AI) journeys, the urgency to deploy effective AI solutions has never been greater. Gen AI in many ways has brought AI to life and yesterday’s impossible seems to be becoming today’s indispensable. The pace of innovation in every layer of the stack continues to accelerate from silicon all the way to systems and beyond. The year 2023 was marked by brainstorming and conceptualizing, but 2024 is all about action and a sense of urgency to not get left behind by the competition.
The technology stack is evolving across every layer.
At the silicon level, we are witnessing groundbreaking AI supercomputing innovations and new technologies from cloud providers, including AI ASICs, advanced networking solutions, and enhanced systems. At the model level, the previous focus on large language models (LLMs) is shifting towards multi-modal models and experiences, often optimized for specific vertical industries.
Additionally, we are seeing the rise of new agents that bring AI to life within enterprise environments. Agents and agent-to-agent systems are revolutionizing enterprises by integrating planning, reasoning, generation, and action with contextual understanding and memory. These capabilities are seamlessly embedded within our systems and software, bringing AI to life in practical applications.
However, as organizations rush to integrate generative AI into their operations, they must avoid repeating past mistakes made during cloud adoption.
The Hyperscale Cloud Providers Conundrum
Hyperscale cloud providers have transformed our approach to computing resources, especially in the realm of Sovereign. However, they come with their own set of challenges:
Localization Issues: Deciding where to host and scale AI initiatives—on-premises or in the cloud—remains a significant challenge.
Vendor Selection: With multiple technology options like GPUs or specialized ASICs available from various vendors, making an informed choice is complex.
Jurisdictional Control: Having local/regional legal control of the data,access and analytics under a local jurisdiction instead of a foreign jurisdiction.
Infrastructure Foundation: Balancing between cloud-native practices and hybrid multi-cloud strategies can be daunting.
Supercomputing Strategies: Managing infrastructure for training and inference requires meticulous planning.
These issues often lead enterprises toward costly reserved instance models due to supply-demand imbalances for specialized resources like GPUs.
IT leaders in enterprises need to build a strategy for supercomputing to support AI development and applications. Are you equipped to build and support HPC stacks and use them for AI-infused applications? Do you want to deliver infrastructures on-premises or in the cloud? Are you able to scale resources in your region? Are you equipped with skills and resources to build and support AI model catalogs? Do you have the engineering practices in place? Lastly, are you planning on keeping your data centers or are you actively migrating to the cloud? Could GenAI be a catalyst to accelerate your cloud migration strategy?
VMware Cloud Foundation & Private AI on VMware Cloud Service Providers: A Robust Alternative
Enterprises need not be confined by these limitations if they leverage VMware Cloud Foundation coupled with Private AI solutions powered by NVIDIA hardware (GPUs/DPUs) on VMware Cloud Service Providers. Here’s how this combination can pave the way for successful implementation of an enterprise’s AI strategy:
Full Stack AI Supercomputing Capabilities
With VMware Cloud Service Providers delivering VMware Cloud Foundation integrated with NVIDIA GPUs/DPUs and hardware AI ASICs, businesses can utilize powerful supercomputers tailored specifically for their needs. VMware Cloud Service Providers are devising AI supercomputing strategies that integrate compute power with specialized networking capabilities (particularly as they often own the networking infrastructure) and purpose-optimized storage layers, complemented by NVIDIA software libraries designed to maximize the productivity of AI supercomputers. This setup mitigates dependency on hyperscale providers’ availability zones and reduces costs associated with reserved instances.
When enterprises initiate their activities in the cloud and assess hyperscale offerings, they first discover the availability—or lack thereof—of cloud-scale AI supercomputing capabilities in their region or country. Secondly, even if sufficient resources are available locally, they often find that the cost of accessing these resources is prohibitively high.
Flexible Infrastructure Foundations
VMware Cloud Service Providers deliver VMware Cloud Foundation a robust cloud capability and allow a seamless integration between their data centers, on-premises infrastructure and other clouds. This hybrid approach ensures that enterprises are not locked into a single vendor ecosystem while maintaining flexibility in deploying workloads where it makes most sense economically and operationally. There is currently a supply-demand imbalance, with demand far outstripping supply for AI supercomputing resources. As a result, enterprise IT teams are finding that hyperscale cloud providers are pushing them towards reserved instance acquisition models and 1 to 3 year commitments, whereas hosting small functional VMware Private AI GPU cluster with a VMware Cloud Service Provider or on-premises is at least an order of magnitude cheaper than utilizing comparable capabilities available in the hyperscale cloud today.
Optimized Model Management
Leveraging an ecosystem of 3rd party or exclusive, production-ready, domain-specific models optimized through collaboration between VMware Cloud Foundation and NVIDIA’s hardware accelerators enables faster training times and more efficient inference processes. This synergy ensures that every ounce of silicon is utilized effectively without compromising performance or scalability. Whilst hyperscale have built models such as Google Gemini, Azure GPT-4, which are differentiators, they are a one size fits all approach, whereas many businesses have very specific needs that only an ecosystem of 3rd party and open-source models can address and particularly need guardrails for their model catalogs.
Enhanced Engineering Platforms
Engineering platforms built using VMware Cloud Foundation and VMware Private AI with NVIDIA offer extensive support for developing comprehensive pipelines that include planning, reasoning, generation actions—all integrated seamlessly within enterprise systems. These platforms facilitate easier management of large-scale deployments across different environments (cloud/on-prem), ensuring consistency in performance metrics irrespective of location constraints.
Cost-Effective Solutions
Utilizing smaller functional GPU clusters in a VMware Cloud Service Provider using VMWare Cloud Foundation and Private AI technologies proves significantly cheaper than comparable offerings from hyperscale providers due largely because there isn’t any premium charged over base compute/storage/network costs unlike typical public clouds which levy additional charges based upon usage patterns etc., thus providing better ROI overall especially when dealing with large datasets needing frequent processing cycles etc..
Scalable Solutions Tailored To Needs
Not every organization requires massive computational power immediately. Many might start small but need scalable options down line once use cases mature further requiring higher capacities eventually. Having ability scale up/down dynamically based actual requirements rather than being forced buy upfront capacity avoids unnecessary expenditures initially thereby allowing smoother transitions phases growth cycle – avoiding potential pitfalls associated premature investments areas yet fully explored/tested adequately beforehand ensuring optimal resource utilization throughout lifecycle project(s).
Conclusion
Every enterprise embarking upon its journey towards becoming future-ready via leveraging advanced technologies such as AI must carefully audit and evaluate choices regarding deployment strategies considering both short term gains and long-term sustainability aspects, equally important factors determining success/failure respective initiatives undertaken therein. By partnering with VMware Cloud Service Providers offering solutions like VMware Cloud Foundation and Private AI with Nvidia customers get a comprehensive solution addressing key pain points faced traditionally during implementations contexts. Businesses stand better chance achieving desired outcomes efficiently cost-effectively ultimately driving transformative changes across board, benefiting stakeholders involved, positively impacting bottom lines significantly over time.
So why wait? Take proactive steps today to ensure tomorrow’s success leveraging best-in-class tools from our VMware Cloud Service Providers designed to keep the specific needs of modern-day enterprises in mind. delivering unparalleled value propositions that are unmatched in the industry!
The post Avoiding the Same Mistakes: Crafting a Successful AI Strategy with VMware Cloud Service Providers appeared first on VMware Cloud Blog.
[[{“value”:”As enterprises transition from ideation to implementation in their artificial intelligence (AI) journeys, the urgency to deploy effective AI solutions has never been greater. Gen AI in many ways has brought AI to life and yesterday’s impossible seems to be becoming today’s indispensable. The pace of innovation in every layer of the stack continues to … Continued
The post Avoiding the Same Mistakes: Crafting a Successful AI Strategy with VMware Cloud Service Providers appeared first on VMware Cloud Blog.”}]] Read More VMware Cloud Blog [#item_full_content]
