The buzz around artificial intelligence (AI) has skyrocketed, with discussions increasing by a staggering 383% since 2022. Yet, despite this surge, a new report from data orchestration firm Hammerspace reveals that a mere fraction of the $65 billion graphics processing unit (GPU) market in 2024 is being harnessed specifically for AI applications.
Titled “State of the Next Data Cycle: How do you GPU?”, the report showcases that organizations are ingeniously venturing beyond AI-specific uses for their GPU investments. David Flynn, founder and CEO of Hammerspace, notes, “The next wave of innovation is being driven by how companies activate their unstructured data.” He adds, “Our research indicates that the GPUs many enterprises initially acquired for AI projects are morphing into versatile tools for data processing, unlocking unexpected value across various industries.”
The report digs into the evolving utilization of GPUs within businesses and reveals that many companies are creatively repurposing these resources for a myriad of non-AI tasks to yield tangible outcomes.
Based on insights drawn from about 17,000 digital conversations involving around 200 industry leaders across platforms like LinkedIn, Twitter, Reddit, GitHub, and Discord, it’s clear that practical applications of AI are lagging behind the growing enthusiasm. Surprisingly, only 18% of innovation discussions are directed toward improving AI outcomes, while a robust 60% of enterprises focus on thought leadership and 59% on productivity gains. Also significant is the 51% of conversations surrounding ethics, notably on policy and best practices, underscoring rising concerns about responsible AI development.
While companies have invested heavily in AI infrastructure, including high-powered GPU chips, they have not yet fully maximized these resources for AI workloads. Increasingly, GPUs are being directed towards more traditional tasks, such as boosting big data and analytics projects. This shift in GPU use is evident across sectors including big tech, scientific research, and media and entertainment, as illuminated in Hammerspace’s findings.
The report includes engaging case studies from notable organizations like Meta Platforms, Los Alamos National Laboratory (LANL), and a well-known streaming service, highlighting varied GPU applications. For instance, Meta deployed over 24,000 NVIDIA H100 GPUs to enhance the training of its Llama 2 and 3 models, focusing on optimizing efficiency and resilience.
LANL has streamlined its hybrid supercomputer environment, integrating CPU and GPU processes to facilitate high-performance computing (HPC) and AI research. This infrastructure supports vital initiatives spanning national security, pandemic preparedness, and climate change efforts. By consolidating siloed file systems into a unified platform, LANL has greatly improved resource utilization and operational efficiency.
Another case study involves a streaming media company that has integrated GPU-CPU combinations to enhance its recommendation algorithms, significantly optimizing video streaming performance for millions. This fusion has not only boosted the speed and accuracy of personalized content recommendations but has also led to remarkable gains in overall streaming quality, as the report indicates.
GPUs: A Resource Beyond AI
This insightful report suggests that organizations are increasingly repurposing GPUs for a variety of existing big data projects, revealing unexpected advantages that extend far beyond AI applications. The GPU market is projected to swell to $274 billion by 2029, yet analysts from Goldman Sachs caution that supply constraints will persist, potentially hindering AI project deployments until at least mid-2025.
A separate survey by Tangoe reveals that 72% of respondents are finding AI-themed cloud spending increasingly unmanageable. This poll, conducted with 500 IT and finance professionals, highlights a striking 30% increase in this category over the past year alone, raising sustainability concerns for companies unsure about their returns on investments in expanded computing capacity.
The AI Buzz Hub team is excited to see where these breakthroughs take us. Want to stay in the loop on all things AI? Subscribe to our newsletter or share this article with your fellow enthusiasts.
