Imagine this. You’re finally ready to deploy your generative AI model. But the moment you do, it suddenly crashes and burns, leaving you to pick up the pieces. This is the last thing you want to happen after months of research and development. What went wrong? More often than not, it’s not the model itself at fault but the infrastructure it was built on. This is why creating a reliable and scalable generative AI infrastructure is crucial to ensure your AI models' efficient deployment and performance in production environments. In this article, we’ll unpack the significance of generative AI infrastructure, common challenges that arise from inadequate infrastructure, and how you can build a robust solution to ensure smooth sailing for your project.
Lamatic’s generative AI tech stack is a valuable tool for achieving objectives such as building a reliable and scalable generative AI infrastructure that can seamlessly handle high computational demands, scale effortlessly with growing data and user needs, and ensure efficient deployment and performance of AI models in production environments.
What is Generative AI Infrastructure and Its Importance
Generative AI infrastructure is the hardware, software, and networking resources required to:
- Develop
- Deploy
- Manage generative AI models
Key components of this infrastructure include:
- GPUs
- Cloud services
- Specialized frameworks
For example, popular frameworks for generative AI include:
- TensorFlow
- PyTorch
Due to their high computational demands, generative AI models would only be practical with this infrastructure. This infrastructure ensures efficient:
- Training
- Scalability
- Real-time performance
This is critical for AI applications across industries. Generative AI infrastructure providers focus on researching and developing the foundational AI techniques, while application developers build products using those foundational technologies.
AI Infrastructure vs IT Infrastructure: What’s the Difference?
Generative AI infrastructure is a subset of AI infrastructure distinct from IT infrastructure. AI infrastructure, an AI stack, refers to the hardware and software needed to create and deploy AI-powered applications and solutions.
Robust AI infrastructure enables developers to effectively create and deploy AI and machine learning (ML) applications like chatbots such as:
- OpenAI’s Chat GPT
- Facial and speech recognition
- Computer vision
Enterprises of all sizes and across various industries depend on AI infrastructure to help them realize their AI ambitions. As enterprises discover more ways to use AI, creating the infrastructure required to support its development has become paramount.
Infrastructure Demands for AI Projects
Whether deploying ML to spur innovation in the supply chain or preparing to release a generative AI chatbot, having the proper infrastructure is crucial. AI projects require bespoke infrastructure primarily because of the power needed to run AI workloads.
AI infrastructure depends on cloud environments' low latency and the processing power of graphics processing units (GPUs) rather than the more traditional central processing units (CPUs) typical of conventional IT infrastructure environments to achieve this kind of power.
AI infrastructure concentrates on hardware and software optimized for cloud-based AI and ML tasks, rather than traditional IT infrastructure, which typically emphasizes:
- PCs
- Software
- On-premise data centers
In an AI ecosystem, software stacks typically include:
- ML Libraries and Frameworks: TensorFlow, PyTorch
- Programming Languages: Python, Java
- Distributed Computing Platforms: Apache Spark, Hadoop
Generative AI Infrastructure Providers: The New AI Ecosystem
Generative AI (GenAI) infrastructure providers are vendors, including cloud platforms and hardware manufacturers, that offer:
- Underlying technology
- Tools
- Hardware
These resources enable companies and developers to build and deploy generative AI applications in production environments. Generative AI refers to technologies capable of creating:
- New, derived versions of content
- Strategies
- Designs
- Methods
These providers offer scalable, reliable, and cost-effective solutions for generative AI projects, which can be complex and expensive to train and deploy.
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How To Build a Reliable and Scalable Generative AI Infrastructure
Deciding on the Right Foundation Model for Your Generative AI Infrastructure
With countless generative AI models available, picking one that aligns with your organization’s goals is critical. As organizations explore the world of foundation models, they’ll find options from several sources, including:
- Proprietary
- Open-source models
Leading providers offer next-generation models as a service, developed through fundamental research and trained on a large corpus of publicly available data. Cloud hyperscalers are also getting into the game by:
- Partnering with the pure-plays
- Adopting open-source models
- Pre-training their models
- Providing full-stack services
It’s worth noting that smaller, lower-cost foundation models (such as Databricks’ Dolly) are making building or customizing generative AI increasingly accessible. All options must be carefully considered to fit your organization’s needs and requests.
Making Generative AI Infrastructure Accessible for Your Organization
Businesses can take two principal approaches to accessing generative AI models:
- Full control
- Managed cloud service
On-Premise Deployment: Pros and Cons
The first option lets organizations deploy models on their public cloud (e.g., cloud hyperscalers) or private infrastructure (e.g., private cloud, data centers). This approach requires identifying and managing the proper infrastructure for these models and developing associated talent and skills. It also entails controlling the models and developing full-stack services for easier adoption.
Alternatively, organizations can opt for speed and simplicity by accessing generative AI as a managed cloud service from an external vendor. Both options have their merits, but if you choose complete control, you must know several additional factors.
Adapting Foundation Models to Your Own Data
Getting maximum business value from generative AI often depends on leveraging your proprietary data to boost:
- Accuracy
- Performance
- Usefulness within the enterprise
Several ways exist to adapt pre-trained models to your data for use within the organization. You can buy an utterly pre-trained model “off the shelf” and use in-context learning techniques to get responses with your data.
Data Foundation for Accelerated AI Value
You can also boost a mainly pre-trained model by adding your data on top through fine-tuning. Of course, you can build your model ground-up (or pre-train further from open-sourced ones) on your infrastructure using your data. To do this at speed and scale, you first need a modern data foundation that makes consuming data through the foundation models easier. This is a prerequisite for extracting accelerated and exponential value with generative AI.
Assessing Your Organization’s Overall Readiness for Generative AI
It’s critical to ensure that foundation models meet the following enterprise requirements:
- Overall security
- Reliability
- Responsibility
Integration and interoperability frameworks are also crucial for enabling full-stack solutions with foundation models in the enterprise. Nevertheless, for AI to be enterprise-ready, organizations must trust it, which raises all sorts of considerations.
Mitigating AI Risks for Sensitive Business Functions
Companies must consider the AI implications of adopting this technology for sensitive business functions. Built-in capabilities from generative AI vendors are maturing, but you must develop your controls and mitigation techniques as appropriate.
Proactive AI Governance for Enterprise Security
Companies can take several practical actions to ensure generative AI doesn’t threaten enterprise security. Adopting generative AI is an ideal time to review your overall AI governance standards and operating models.
Considering the Environmental Impact of Generative AI
Although they come pre-trained, foundation models can still require significant energy during adaptation and fine-tuning. This becomes very significant if you consider pre-training your model or building it from the ground up.
Environmental Impact of Foundation Model Adoption
Different implications depend on the approach to:
- Buying
- Boosting
- Creating the foundation models
Left unchecked, scaling up applications based on generative AI across the enterprise will significantly impact the organization’s carbon footprint. So, the potential environmental impact needs to be considered upfront in making the right choices about the available options.
Industrializing Generative AI App Development
After choosing and deploying a foundation model, companies must consider what new frameworks may be required to industrialize and accelerate application development. Vector databases or domain knowledge graphs that capture business data and broader knowledge (such as how business concepts are structured) also become essential for developing valuable applications with generative AI.
Industrializing Prompt Engineering for Competitive Advantage
Prompt engineering techniques are fast becoming a differentiating capability. By industrializing the process, you can build a corpus of efficient, well-designed prompts and templates aligned to specific business functions or domains. Look to incorporate enterprise frameworks to scale collaboration and management around them.
An orchestration framework is key for application enablement, as stitching together a generative AI application involves coordinating multiple components, services, and steps.
Understanding What It Takes to Operate Generative AI at Scale
Consider their impact on operability as your generative AI applications launch and run. Some companies have already developed an MLOps framework to productize ML applications.
Those standards require a thorough review to incorporate LLMOps and Gen AIOps considerations and accommodate changes in DevOps, CI/CD/CT, model management, model monitoring, prompt management, and data/knowledge management in pre-production and production environments.
The MLOps approach will have to evolve for the world of foundation models, considering processes across the whole application lifecycle. As generative AI leads to AutoGPT—where AI powers much more of the end-to-end process—we’ll witness AI driving an operations architecture that automates:
- Productionizing
- Monitoring
- Calibrating
These models and their interactions ensure the continued delivery of business SLAs.
Lamatic: Your Managed GenAI Tech Stack
Lamatic offers a managed Generative AI tech stack that includes:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low-Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge Deployment via Cloudflare Workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our managed generative AI tech stack.
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Why Is a Comprehensive Tech Stack Essential in Building Effective Generative AI Systems?
Machine Learning Frameworks: The Backbone of Generative AI
Generative AI systems rely on complex machine learning models to create new data. Machine learning frameworks provide the functionality to build and train models, including:
- TensorFlow
- Keras
- PyTorch
These frameworks offer APIs and tools for different tasks and support a variety of pre-built models for:
- Image
- Text
- Music generation
This flexibility allows users to design and customize models to achieve the desired level of accuracy and quality. These frameworks should be integral to the generative AI tech stack.
Programming Languages: Building the Generative AI System
Programming languages are crucial in building generative AI systems that balance ease of use and the performance of generative AI models.
Python is the most commonly used language in machine learning and is preferred for building generative AI systems due to its:
- Simplicity
- Readability
- Extensive library support
Other programming languages, like R and Julia, are also sometimes used.
Cloud Infrastructure: Powering Generative AI Applications
Generative AI systems require large amounts of computing power and storage capacity to train and run the models. Including cloud infrastructures in a generative AI tech stack is essential, providing the scalability and flexibility needed to deploy generative AI systems.
Cloud providers offer services, including virtual machines, storage, and machine learning platforms, such as:
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure
Data Processing Tools: Making Data Ready for Generative AI
Data is critical in building generative AI systems. The data must be preprocessed, cleaned, and transformed before it can be used to train the models. Data processing tools commonly used in a generative AI tech stack for efficiently handling large datasets include:
- Apache Spark
- Apache Hadoop
These tools also provide data visualization and exploration capabilities, which can help understand the data and identify patterns.
Get Started With Generative AI Today
A well-designed generative AI tech stack can improve the system's:
- Accuracy
- Scalability
- Reliability
This enables faster development and deployment of generative AI applications.
How to Optimize Generative AI Infrastructure Costs
Generative AI Workloads: The High Cost of Doing Business
Generative AI workloads consume:
- Specialized hardware:
- GPUs
- TPUs
- AI-optimized CPUs
- Scalable storage
- Seamless networking
These technologies come with a price tag that can catch organizations off guard if they do not have a cost optimization strategy. Without cost controls, organizations risk overspending or misallocating resources, eroding return on investment.
The Stubborn Facts About Generative AI
It has been said that facts are stubborn things. A stubborn fact for generative AI is that it consumes large quantities of:
- Compute cycles
- Data storage
- Network bandwidth
- Electrical power
- Air conditioning
Many launch cloud-based or on-premises initiatives as CIOs respond to corporate mandates to “just do something” with generative AI. But while the payback promised by many generative AI projects is nebulous, the costs of the infrastructure to run them are finite, and too often, unacceptably high.
Outsized Growth of Generative AI
Infrastructure-intensive or not, generative AI is on the march. According to IDC, generative AI workloads are increasing from 7.8 percent of the overall AI server market in 2022 to 36 percent in 2027. In storage, the curve is similar, with growth from 5.7 percent of AI storage in 2022 to 30.5 percent in 2027.
IDC research finds roughly half of worldwide generative AI expenditures in 2024 will go toward digital infrastructure. IDC projects the worldwide infrastructure market (server and storage) for all kinds of AI will double from $28.1 billion in 2022 to $57 billion in 2027.
The Unsustainable Costs of Generative AI
The infrastructure needed to process generative AI’s large language models (LLMs) and power and cooling requirements is quickly becoming unsustainable. “You will spend on clusters with high-bandwidth networks to build almost HPC [high-performance computing]-like environments,” warns Peter Rutten, research vice president for performance-intensive computing at IDC. “Every organization should think hard about investing in a large cluster of GPU nodes,” says Rutten, asking, “What is your use case? Do you have the data center and data science skill sets?”
A Strategic Approach to Generative AI Infrastructure
Savvy IT leaders know the risk of overspending on generative AI infrastructure, whether on-premises or in the cloud. After looking hard at their physical operations and staff capabilities and the fine print of cloud contracts, some are developing strategies that deliver a positive return on investment.
A Generative AI Success Story
Dr. Mozziyar Etemadi, Medical Director of Advanced Technologies at Northwestern Medicine, launched a generative AI initiative to accelerate X-ray interpretation to address the growing demands on understaffed radiology teams. Rather than relying on massive, resource-intensive large language models (LLMs), his team adopted a more efficient approach using small language models (SLMs), significantly reducing infrastructure requirements.
Initial experiments with cloud-based services proved too costly and complex, prompting Etemadi to lead a dedicated in-house engineering effort. The team built a four-node cluster of Dell PowerEdge XE9680 servers equipped with Nvidia H100 GPUs and connected via Quantum-2 InfiniBand networking. Housed in a colocation facility, the system processes multimodal data, images, text, and video to train the SLM in medical image interpretation.
From X-rays to MRIs: Expanding AI’s Reach
The resulting application, now patented, delivers high-accuracy outputs that human clinicians review for final decisions. Despite its power, the model remains lightweight, with only 300 million parameters compared to LLMs like ChatGPT with over a trillion. Etemadi plans to expand the tool’s capabilities to cover:
- CT scans
- MRIs
- Colonoscopy data
By running the infrastructure in-house, Northwestern Medicine has cut operating costs by roughly 50% compared to cloud-based alternatives. “Pretty much any hospital in the U.S. can buy four computers,” Etemadi notes. “It’s well within the budget.”
Storage Strategies for Generative AI
Regarding data storage, Northwestern Medicine uses both the cloud and on-premises infrastructure for temporary and permanent storage. “It’s about choosing the right tool for the job. With storage, there is no one-size-fits-all,” says Etemadi, adding, “As a general rule, storage is where cloud has the highest premium fee.”
Northwestern Medicine uses a mix of Dell NAS, SAN, secure, and hyperconverged infrastructure equipment on premises. “We looked at how much data we needed and for how long. Most of the time, the cloud is not cheaper,” asserts Etemadi.
The Cost Calculus of GPU Clusters
Faced with similar challenges, Papercup Technologies, a UK company that has developed generative AI-based language translation and dubbing services, took a different approach. Papercup clients seeking to globalize the appeal of their products use the company’s service to generate convincing voice-overs in many languages for commercial videos.
Before a job is complete, an HITL examines output for accuracy and cultural relevance. The LLM work started in a London office building, which was soon outgrown by the infrastructure demands of generative AI.
Beyond the Price Tag: Operational Challenges of On-Prem AI
“It was quite cost-effective at first to buy our hardware, which was a four-GPU cluster,” says Doniyor Ulmasov, head of engineering at Papercup. He estimates initial savings between 60 and 70 percent compared with cloud-based services. “But when we added another six machines, the power and cooling requirements were such that the building could not accommodate them. We had to pay for machines we could not use because we couldn’t cool them,” he recounts.
And electricity and air conditioning weren’t the only obstacles. “Server-grade equipment requires know-how for things like networking setup and remote management. We expended a lot of human resources to maintain the systems, so the savings weren’t there,” he adds.
Powering the Future of Media with Hybrid AI Infrastructure
At that point, Papercup decided the cloud was needed. The company now uses Amazon Web Services, where translation and dubbing workloads for customers are handled, to be reviewed by an HITL.
Simpler training workloads are still on premises on a mixture of servers powered by Nvidia A100 Tensor Core, GeForce RTX 4090, and GeForce RTX 2080Ti hardware. More resource-intensive training is handled on a cluster hosted on Google Cloud Platform. Building on its current services, Papercup is exploring language translation and dubbing for live sports events and movies, says Ulmasov.
For Papercup, geography and technology requirements drive infrastructure decisions as much as. “If we had a massive warehouse outside the [London] metro area, you could make the case [for keeping work on-premises]. But we are in the city center. I would still consider on-premises if space, power, and cooling were not issues,” says Ulmasov.
Beyond GPUs: The Future of Generative AI Infrastructure
For now, GPU-based clusters are faster than CPU-based configurations, which matters. Etemadi and Ulmasov say using CPU-based systems would cause unacceptable delays that would keep their HITL experts waiting. The high energy demands of the current generation of GPUs will only increase, according to IDC’s Rutten.
“Nvidia’s current GPU has a 700-watt power envelope, then the next one doubles that. It’s like a space heater. I don’t see how that problem gets resolved easily,” says the analyst.
An emerging host of AI co-processors and, eventually, quantum computing could challenge GPUs' reign in generative AI and other forms of AI.
Beyond GPUs: What’s Next for AI Infrastructure
“The GPU was invented for graphics processing so it’s not AI-optimized. Increasingly, we’ll see AI-specialized hardware,” predicts Claus Torp Jensen, former CIO and CTO and currently a technology advisor.
Although he does not anticipate the disappearance of GPUs, he says future AI algorithms will be handled by a mix of CPUs, GPUs, and AI co-processors, both on-premises and in the cloud.
Energy-Efficient AI: From SLMs to Specialized Chips
Another factor working against unmitigated power consumption is sustainability. Many organizations have adopted sustainability goals, but power-hungry AI algorithms make it difficult to achieve them. Rutten says using SLMs, ARM-based CPUs, and cloud providers that maintain zero-emission policies or run on electricity produced by renewable sources is worth exploring, where sustainability is a priority.
For implementations that require large-scale workloads, using microprocessors built with field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs) is a choice worth considering.
Making Generative AI Work Within Real-World Limits
“They are much more efficient and can be more powerful. You have to hardware-code them up front, and that takes time and work, but you could save significantly compared to GPUs,” says Rutten.
Until processors that run significantly faster while using less power and generating less heat emerge, the GPU is a stubborn fact of life for generative AI. Implementing cost-effective generative AI implementations will require ingenuity and perseverance. But as Etemadi and Ulmasov demonstrate, the challenge is not beyond the reach of strategies utilizing small language models and a skillful mix of on-premises and cloud-based services.
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack. Their solution provides managed GenAI middleware, custom GenAI API (GraphQL), low-code agent builder, automated GenAI workflow (CI/CD), GenOps (DevOps for GenAI), edge deployment via Cloudflare workers, and integrated vector database (Weaviate).
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Their platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with Lamatic’s managed generative AI tech stack.
