I was at an investor conference the other week and in several of my twelve back-to-back 1:1 meetings I was asked something to the effect of, “Wait, this is the first conversation I have been in this week where I wasn’t beaten over the head by how your company is ‘all about AI’ and that everything is ‘going to be AI’ and that ‘AI is the only future’ or my favorite, ‘data centers are dead and so 2000s, it’s all about the AI center now’” It reminds me of the 2009-2012 punditry around cloud where I was literally told, “if the answer isn’t ‘cloud’ then the question is wrong!” My thesis here is simple: Reality and punditry often never cross paths and may never meet each other.
See, in the rapidly evolving landscape of Artificial Intelligence processing, Qumulo has emerged as a pivotal player, particularly in mainstream enterprise AI training and inference clusters. With AI applications expanding into domains like image recognition, genomics, proteomics, autonomous driving, and advanced industrial modeling, Qumulo’s influence is both broad and deep, granted many of these workloads two years ago would have been called ‘supercomputing’ ‘deep learning’ ‘HPC’ or ‘machine learning’ but today have all been recast under an umbrella of ‘AI’ to keep the pundits at bay. The AI training market, despite its vast potential, is characterized by a limited number of sizable opportunities that can be categorized into three main buckets:
First, nation-states leverage AI for cryptographic research and image recognition within national intelligence and defense sectors. This application of AI is crucial for maintaining global/national-interest security and advancing cryptographic techniques that ensure the confidentiality and integrity of sensitive information or compromise that of non-aligned nation-states.
Secondly, the financial services sector, particularly hedge funds, is adopting AI to create trading alpha and generate outsized returns. By harnessing AI, these institutions can execute complex trading strategies, analyze vast datasets in real-time, and adapt to market conditions faster than ever.
Third and simultaneously, large technology ‘alphas’ focus on AI model development and training to enhance ad-serving models, dynamic content delivery, and deep learning algorithms that provide a deeper understanding of user procurement patterns and socio-demographic behaviors.
However, a significant bottleneck across all these AI initiatives is the availability of human capital. The talent pool capable of developing, tuning, updating, and maintaining these sophisticated models is limited. This scarcity of skilled developers presents a formidable challenge to the advancement and scalability of AI technologies.
At Qumulo, we are fortunate to be embedded in many of the world’s most significant AI training, inference, and powerful computing clusters, contributing to managing and delivering unprecedented volumes of data—billions of files and objects—processed through specialty processor-powered environments. While our role in these large-scale operations is critical, our primary focus lies in the mainstream enterprise application of AI technologies. Given the constraints in human capital and the dearth of available resources, we are witnessing a growing trend among enterprise customers: our clients are increasingly utilizing cloud-hosted specialty processors to execute customized tuning of existing open-source or commercially available AI models. This approach allows them to optimize limited resources while maximizing the value derived from AI investments.
One of the critical enablers of this trend is the adoption of Retrieval Augmented Generation (RAG) architectures supported by pre-trained models and the embedding of customer private data stored in local vector databases. When integrated with 250PB to 500PB or larger clustered storage systems developed by Qumulo, these tools drive significant advancements in fields such as autonomous driving systems, mapping databases, genomic modeling, and early identification of degenerative neural diseases and early-stage cancer detection. We broadly categorize this trend as ‘Applied AI,’ where open-source or commercially available AI models are utilized with enterprise data to produce accurate, tunable, and valuable outputs at high volume and high velocity.
Qumulo’s unique value proposition lies in our ability to provide incredibly high-volume file and object storage in the public, private, or hybrid cloud environment and with our Cloud Data Platform and its ability to
In conclusion, as AI permeates various industries, managing and processing vast amounts of data efficiently and effectively becomes increasingly critical. Qumulo’s Cloud Data Platform, combined with our focus on applied AI, positions us as a leader in enabling enterprises to harness the full potential of AI technologies. By overcoming the limitations posed by human capital and leveraging the scalability and flexibility of the cloud, we are helping our clients unlock new levels of innovation and productivity in their AI endeavors.
While we can be pragmatic about the long-term market for AI training and ask rational questions like, “Will this massive investment ever generate a return for the business?” It is also possible to support our clients and help them deliver the desired outcomes, but at a cost point and with an elastic acquisitions model that can support both their AI vision and their profitability/efficiency imperative.
