Barriers to Investing in Compute and Exabits' Solutions

While the concept of pooling random devices into a unified network for AI workloads sounds appealing, the reality is far more complex.

As compute power becomes the driving force behind advancements in AI and other major technologies, it is quickly becoming the currency of the future. However, investing in compute comes with high barriers - the reality is that you can either trade NVIDIA stock or invest in GPUs. But this is more complex than it seems. While the concept of pooling random devices into a unified network for AI workloads sounds appealing, the reality is far more complex. This blog explores what is involved in investing in compute in a real-world context.

Challenges of Spinning up AI Ready Compute

• Cost: Even a small cluster of H100 machines is upward to $10M

• GPU Scarcity: Unallocated or “underutilized” H100, A100, RTX4090 GPUs are almost non-existent.

• Very Specific Physical & Technical Requirements: A data center with the right infrastructure is also very rare. There are only 10 in North America that deploy thousands of GPUs, in the open market.

• Domain Expertise: Teams of experts with the years of experience required to operate, deploy and manage AI compute at scale are as rare as the GPUs they deploy.

“To build and run a large NVIDIA training cluster takes a lot of skill. Giga Texas will be 50,000…Imagine 50,000 instruments all moving in symphony. The power fluctuations are like nothing you have seen anywhere. When the GPU’s go up the power goes up, when they go down….this happens at 100 millisecond intervals.” -Elon Musk

Standing up Real AI Compute is Serious Business

Random Devices from Random Places Serving Serious AI = Not a Real Thing

As sexy as it sounds, the narrative of aggregating random devices into a powerful compute network that serves serious AI training and inference workloads does not work. Real AI applications demand enterprise-grade GPUs from AI ready data centers (AIDC) which are optimized for heavy AI tasks. Exabits addresses this need by providing robust and reliable performance through their optimized infrastructure.

System-Level Optimization

Simply acquiring a large number of high-end GPUs, like the NVIDIA H100, does not guarantee optimal performance. Significant system-level optimization is required, which comes from years of experience. Exabits employs proprietary software to accelerate, stabilize, and optimize GPU operations, leveraging extensive system-level optimization expertise to ensure seamless performance.

Infrastructure Requirements

Even with access to thousands of high-end GPUs, specific physical and technical conditions are necessary for effective operation. This includes advanced cooling systems and robust power supplies, which are integral to Exabits' AI-ready data centers (AIDC). These centers provide the necessary environment to support extensive AI compute hardware.

High Costs

Setting up an AI GPU cluster involves significant capital investment. For instance, a single NVIDIA H100 GPU costs around $35,000, and an AI-ready machine typically requires eight H100 GPUs, bringing the cost to approximately $300,000 per machine. Building a small cluster of 32 such machines can easily cost between $8 million and $10 million, excluding additional expenses for connectivity, optimization, and cooling hardware. Exabits' extensive supply of enterprise-grade GPUs and optimized infrastructure reduces these costs, making high-performance compute more accessible.

Limited Availability of AI-Ready Data Centers

Large-scale AI-ready data centers are scarce, with fewer than ten available in the open market. These data centers are essential for providing the necessary conditions for AI compute operations. Exabits' AI-ready data centers (AIDC) ensure the optimal environment for AI compute operations, supporting the extensive hardware required.

Specialized Expertise

Effective AI compute operations require highly specialized expertise, which is rare. These experts are crucial for navigating the complexities of AI infrastructure and optimizing high-performance compute environments. Exabits' team comprises domain experts with decades of experience in deploying, operating, and managing these environments, ensuring efficient and effective handling of AI workloads.

Software Solutions

Successful AI compute operations necessitate sophisticated software for linking, stabilization, and acceleration. This software is essential for ensuring seamless operation and peak efficiency of all GPUs. Exabits' proprietary software optimizes the interaction between GPUs, storage systems, and network resources, enabling AI applications to run smoothly and efficiently.

High-Performance GPUs

Only a limited number of GPU models are suitable for the demanding tasks associated with AI compute operations. Less than 1% of GPUs on the market are regarded as viable for these purposes. Exabits ensures the availability of high-performance GPUs like the H100, A100, and RTX 4090, meeting the demanding requirements of AI workloads.

Supply Constraints

High-end GPUs like the H100 are in limited supply, with most allocated to major industry players. For example, of the 1.5 million H100 GPUs NVIDIA plans to ship in 2024, 95% are already allocated to giants like Google, AWS, and OpenAI. This scarcity leads to lengthy waiting lists for potential buyers. Exabits' unmatched supply chain capabilities and extensive inventory of enterprise-grade GPUs overcome these constraints, ensuring availability for clients.

Exabits: Deploying the Compute Economy

Exabits is the sole team in the Web3 space capable of deploying, interconnecting, and operating thousands of H100 GPUs independently. Our unmatched expertise and resources position us uniquely to lead the compute economy, transforming how AI workloads are managed and optimized globally. By creating a dynamic and interconnected ecosystem, Exabits empowers node and subnet participants, ensuring a self-sustaining cycle of growth and innovation. Node investors play a crucial role in expanding compute capabilities, enhancing the ecosystem. Subnet participants receive compute and are incentivized to optimize their resources, driving continual improvement.

Power to the People: The AI Compute Economy

Exabits is committed to democratizing AI compute, giving everyone the ability to own a piece of AI compute. This model decentralizes power and provides a voice in how AI is developed, deployed, and operated. With affordable entry points, demand-driven subnets, and reward systems, Exabits ensures that the benefits of AI compute are widely accessible. Exabits makes ownership of high-performance compute available to everyone, providing a voice in AI development and operation, fostering a collaborative environment.

Conclusion

Exabits is revolutionizing the AI compute economy by leveraging a robust, interconnected ecosystem of node and subnet participants. With a focus on accessibility, optimization, and democratization, Exabits is transforming the landscape of AI compute, making it possible for anyone to participate and benefit from AI. By creating a dynamic and interconnected ecosystem, Exabits is empowering individuals and communities to play a crucial role in the development and deployment of AI, ensuring that the future of AI is shaped by diverse and inclusive perspectives. In this evolving landscape, compute is the currency of the future, and Exabits is creating the landscape for it.