Since OpenAI introduced ChatGPT in December 2022, the world has been swept up in the wave of Generative AI. Enterprises are now actively exploring how to leverage AI to increase productivity, streamline operations, and gain a competitive edge in an increasingly digital-first world.  Software development, IT management, and customer support were among the first to feel the impact. 

As enterprises began to adopt these AI solutions, a critical challenge emerged: the handling of proprietary data. Many businesses operate with sensitive or confidential information that they are unwilling to share with third-party API providers. This concern is particularly acute in industries like healthcare, finance, and legal services, where data privacy and security are paramount.  Solutions like Retrieval Augmented Generation (RAG) do not fully address this problem.

Luckily, we are witnessing a significant acceleration in open-source innovation.  This week, DeepSeek’s release of its R1 models has garnered attention for their ability to achieve results similar to ChatGPT-o1 while using far less computational power for training.  DeepSeek is open source and open weights, which will lead to further innovation and optimization of others in the open source community in the coming months.  These advancements are making it increasingly feasible for enterprises to fine-tune open-source models using their own proprietary data and deploy them in their own environments, solving the data privacy and security problems, while possibly enjoying better cost efficiency.

The infrastructure supporting these AI workloads is also undergoing a significant transformation. Traditional data centers, built around X86-centric architectures, are being reimagined to accommodate the unique demands of AI. At the heart of this shift is the move toward GPU-centric architectures, which are better suited to handle the parallel processing requirements of AI workloads.

However, this transition is not without its challenges. Unlike traditional CPUs, GPUs lack a mature software layer for virtualization, resource pooling, and sharing. This creates a critical gap in the ability to efficiently allocate and manage GPU resources across different users and workloads within an enterprise. As AI workloads grow in complexity and scale, this gap becomes increasingly apparent, necessitating a new approach to AI infrastructure management.

At its core, AI Infra Automation Software consists of three key components:

Enterprise GPU-as-a-Service Software
This foundational layer manages GPU clusters, enabling efficient pooling and sharing of GPU resources. It automatically maps these resources to AI workloads based on demand, ensuring optimal utilization and performance.

Inference-as-a-Service Software
Built on top of the GPU-as-a-Service layer, this component automates the deployment of popular AI models, such as Llama or DeepSeek, for enterprise users. It also supports auto-scaling, dynamically adjusting the number of model-serving nodes as demand fluctuates.

Fine-tuning-as-a-Service Software
This layer provides AI developers with the tools they need to experiment with, fine-tune, and test AI models. It simplifies the process of customizing models to incorporate proprietary data or to meet specific business needs, accelerating the development cycle.