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Problem

The Problem and Our Vision​

The AI revolution is in the early stage of development, with virtually uncapped demand for the GPUs that power computing for AI model training, fine tuning, and inferencing. Much of this demand is driven by the major models built by Open AI, Meta, Anthropic and Google, and powered by hyperscalers' data centers. However, there is also significant demand for compute by VC-backed companies for small model training, fine tuning of open source models and inferencing. These smaller players want access to cheap, fast and reliable GPU clusters. The hyperscalers' response: fast and reliable, yes. Cheap? No. In response, customers are turning to Web2 and Web3 players who crowdsource GPU supply across a distributed network of GPU owners, ranging from people with an idle GPU on their laptop to datacenter operators looking to monetize the AI revolution with their own supply of GPUs. Many of these providers are as much as 70% cheaper than AWS and the other hyperscalers.

Berkeley Compute is building tools that empower thousands of GPU operators, and millions of GPU owners, to run their own business – large or small – by joining our compute network. At Berkeley Compute, we believe that permissionless distributed computing networks unlock the lowest cost at the highest quality possible, for the same reasons that we see proof of work blockchain nodes discover the lowest cost of power, rent and human resources. We also believe that using blockchain rails to connect, set up and settle payments between GPU customers and GPU operators is the lowest cost and least friction. Further, we believe blockchains provide transparency, price discovery and quality discovery better than any Web2 distributed compute network. Additionally, while we believe in permissionless networks' ability to source low cost compute, we generally believe that customer demand for reliable and stable compute will land most of the supply side in datacenters run by seasoned professionals. Finally, we believe that by tokenizing GPUs and their cash flows, we can massively scale Berkeley Compute's supply side with crowdsourced capital, and unlock new DeFi capabilities with collateralized lending, commodities trading of compute, and new investment funds similar to mortgage backed securities and real estate investment trusts (REITs).

Customer problems​

  • Cost and availability of reliable GPU compute
    • Smaller AI companies and researchers struggle to access the GPU resources they need due to high costs and limited availability.
    • Hyperscale cloud service providers use vendor lock in, quotas, waitlists, and barriers and prioritize lifetime customer value over ease of use
  • Centralization of AI Compute Resources
    • The concentration of GPU resources among a few large tech companies creates a barrier to innovation and raises concerns about the democratization of AI development.
    • This centralization can lead to potential bottlenecks in AI advancement and ethical concerns regarding the control of AI capabilities.
  • Provider Quality and Reliability Concerns
    • The decentralized nature of GPU provision raises concerns about the consistency and reliability of service quality across different providers.
    • There's a lack of standardized performance metrics and service level agreements (SLAs) for GPU compute resources in a distributed network.
    • Users may hesitate to rely on decentralized GPU resources for critical AI workloads due to uncertainties about uptime, performance stability, and data security.

Operator problems​

  • Capital Intensive Nature of GPU Infrastructure
    • Setting up and maintaining GPU clusters requires substantial upfront investment and ongoing operational costs.
    • Pool operators (data center owners) face challenges in scaling their operations due to capital constraints.
  • Lack of Liquidity in GPU Infrastructure Investments
    • Traditional investments in GPU infrastructure are illiquid, with limited options for partial ownership or easy transfer of assets.
    • There's no established secondary market for GPU cluster investments, reducing flexibility for investors.
  • Complexity in Managing and Scaling GPU Resources
    • For pool operators, managing multiple GPU clusters, negotiating contracts, and handling payments from various clients is complex and time-consuming.
    • AI workload customers face difficulties in efficiently scaling their compute resources up or down based on demand.

Berkeley Compute's decentralized protocol and tokenized GPU clusters directly address these problems, creating a more efficient, accessible, and liquid market for GPU compute resources. By solving these issues, we aim to accelerate AI innovation, democratize access to high-performance computing, and create new investment opportunities in the rapidly growing AI infrastructure market.