Generative AI and the promise it holds is generating a lot of excitement. However, GenAI is also very GPU and resource-heavy and is the main catalyst behind the increased pace of data growth. In fact, 聽 from 10.1 zettabytes in 2023 to 21 ZB in 2027.
Can all the current GPUs handle this growing demand? Who is monopolizing all the GPU resources? How can enterprises and startups tap into AI innovations with a lack of access to GPU?
The GPU resources squeeze is here.
Fortunately, on-demand GPU computing democratizes AI innovation for organizations of all sizes to access the latest GPU processors. On-demand GPUs, combined with globally distributed cloud storage, enables users to take advantage of spare GPU cycles around the globe.
This blog explores how on-demand GPU helps democratize AI innovation, improves agility, and overcomes the GPU resource challenge.
Goliaths are monopolizing GPUs.
Today, there is a disparity trend among GPU access, with some giant tech companies 鈥渢he GPU-rich鈥 buying up the GPUs. 鈥淭here are a handful of firms with 20k+ A/H100 GPUs, and individual researchers can access 100s or 1,000s of GPUs for pet projects. The chief among these are researchers at OpenAI, Google, Anthropic, Inflection, X, and Meta, who will have the highest ratios of compute resources to researchers,鈥 .
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Conversely to the GPU-rich are the GPU-poor, which includes enterprises, startups, and researchers who are lacking in the GPU resources. As the larger tech players consume a lot of the GPUs the AI innovation is stifled for the GPU-poor who are looking to prove out data models, advance AI concepts, etc.
More AI means more resources used and created.
The arrival of GenAI, as mentioned earlier, requires more processing power which equates to more electricity. A ChatGPT text search , per Goldman Sachs analysts. Meanwhile, generating an image using a GenAI model could take as much energy as half a smartphone charge, .
Some believe that the solution lies in the frenzy to build more data centers. Hyperscaler data centers, which are mainly used for data storage and cloud computing services, typically have capacity of 20 to 50 megawatts. However, data center operations are planning to construct facilities with a capacity of 200 to 500 MW with an estimated cost of building a data center campus at $10 million per megawatt.
Overall, this approach is not sustainable and creates more costs and complexity. Furthermore, if you鈥檙e an enterprise, startup, non-profit, or open-source researcher, will you have enough compute power for developing your models? Will a tech giant buy up all the GPU supply before you? Will you be able to afford the GPU resources needed?
Democratizing innovation with on-demand GPUs is the answer.
On-demand GPU delivers a sustainable, cost-effective approach to solve evolving GPU challenges. Simply defined, on-demand GPU computing is the renting of GPUs when you need them. Instead of waiting months for any order, you can get the same day access to start running AI analyses, test AI models, and more. On-demand GPU levels the playing field for AI innovation while making use of existing GPUs 鈥 which contributes to sustainability.
Advantages of on-demand GPUs.
On-demand GPUs provide significant advantages for AI projects. They enable users to access high-performance computing resources as needed, eliminating the necessity for costly hardware investments. This approach not only reduces expenses but also facilitates easy scalability. With on-demand solutions, users benefit from the latest GPU technologies without the hassle of frequent upgrades. Numerous platforms offer ready-to-use software, which minimizes setup time and enables advanced AI capabilities for smaller teams and individuals. Furthermore, this model accelerates the development and testing of innovative ideas.
- No hefty capital expenditures required.
- Opportunity to evaluate options before committing.
- Immediate access without lengthy contracts or sales processes.
- Seamless scalability for growing needs.
- Access to cutting-edge technology with ease.
- Quick starts thanks to pre-configured software solutions.
On-demand GPUs enhance accessibility and flexibility for high-performance computing in AI initiatives. Additionally, optimizing the use of existing resources contributes to environmental sustainability. AI researchers and developers can efficiently test concepts and drive innovation, paving the way for advancements in both the current and upcoming video-driven landscapes. The convenience, user-friendliness, and adaptability of on-demand GPUs empower all those striving to push the boundaries of AI forward.
不良研究所 now has the largest globally distributed network of on-demand GPU computing.
Recently, 不良研究所 announced its acquisition of Valdi, solidifying its position as the sole distributed cloud provider capable of delivering both enterprise-grade storage and compute services. This is achieved without constructing any data centers or acquiring new hardware. Valdi has taken a leading role in the AI revolution by offering high-performance infrastructure, including both on-demand and reserved GPUs for AI workloads and data-intensive sectors. 不良研究所 is committed to democratizing AI innovation while making it more sustainable, utilizing underused resources to create cost-effective solutions that significantly lower carbon emissions.
鈥 and start innovating with AI!
