Distributed AI Inference: Strategies for Success

As AI models continue to evolve into operational cornerstones for enterprises, real-time inference has emerged as a critical engine driving this transformation. The demand for instantaneous, decision-ready AI insights is surging, with AI agents – rapidly becoming the vanguard of inference – poised for explosive adoption. Industry forecasts suggest a tipping point, with over half of enterprises leveraging generative AI expected to deploy autonomous agents by 2027, according to Deloitte. In response to this trend, enterprises are seeking scalable and efficient ways to deploy AI models across multiple servers, data centers, or geographies, and are turning their gaze to distributed AI deployments in the cloud. 

In a previous blog, Distributed AI Inference – The Next Generation of Computing, I covered the basics of distributed AI inference and how leveraging Akamai Cloud’s uniquely high-performance platform can help businesses scale at an impressively low cost. In this blog, we’ll continue to explore concepts around distributed AI inference, in particular, how to deploy, orchestrate, and scale AI using a distributed cloud architecture. Plus, we’ll go into the challenges associated with such a model. 

Deployment

You’d be right if you think that deploying AI models on a global scale sounds like a complicated affair. Fortunately, there are swathes of tools and technologies available to support the full lifecycle of AI from its inception and training to deployment, refinement, and management. Choosing the right mix of solutions requires careful consideration. Akamai Cloud partners with many leading technology vendors to provide the fundamental components of AI inference and a vibrant ecosystem. We’re building the AI inference cloud for today while future-proofing for tomorrow by delivering a range of computing power, data storage, and management solutions close to your users, along with the software needed to connect your models across distributed sites.

AI Inference on Akamai Cloud integrates powerful technologies and leverages partnerships with leading vendors to create a high-performance ecosystem for delivering AI at speed. This includes the following:

• Model serving using inference engines like Nvidia Dynamo (previously Triton) and Kserve, enabling seamless access to AI models for your applications.
• MLOps and orchestration with tools like KubeFlow, Nvidia Rapids, and KubeSlice to support data pipelines, model lifecycle management, and performance monitoring.
• Model optimization with technologies such as the Nvidia TAO toolkit and KubeFlow, enabling fine-tuning, pruning, quantization, and other model optimization techniques.
• Data management through key integrations with data fabric platforms, databases, and libraries, like VAST Data, Nvidia Rapids, and Milvus, for storing, processing, and transferring data tied to AI workloads, as well as providing governance capabilities for model lineage, versioning, and explainability.
• Edge computing on Akamai’s global edge network, with partners like Fermyon and Avesha providing lightweight compute to drastically reduce latency and improve performance.
• AI Gateway provides a unified endpoint for routing requests from applications/users at the edge to the AI model(s), with capabilities to optimize security, performance, resilience, and accessibility for developers and AI agents.  

Underpinning all of the above is Akamai Cloud, delivering the core infrastructure for compute, storage, networking, containerization, and enterprise-grade security and reliability to power your AI models across distributed cloud infrastructure. 

I want to take a moment to highlight model optimization, a crucial process when distributing AI. Techniques like model pruning (to remove redundant parameters) and quantization (to reduce precision with minimal impact on the overall inference accuracy) play an important role in preparing a model to run closer to edge locations where compute resources may be limited. This helps to ensure that autonomous systems, such as AI agents, can deliver fast decisions and responsive outputs, despite constrained compute resources. For agent-driven workloads that require rapid environmental analysis and iterative planning, your AI engineers may also be looking into advanced techniques like model sharding, dynamic request matching, and splitting models to run multistep inference in parallel to further optimize latency and price performance across distributed deployments. 

Leveraging these optimization techniques can: 

• dramatically reduce model size, sometimes by up to 80%, making it far more lightweight to deploy,
• reduce computational cost and energy consumption, making the model more efficient to operate,
• improve inference speed significantly, particularly useful for latency-sensitive applications.

Improving model efficiency and performance with these methods and deploying models on a distributed architecture with proximity to users and data, reduces the cost and latency barriers to deploying enterprise AI applications. 

Scaling

Scaling is crucial to the success of AI inference, especially if you’ve built a successful model that actually garners the interest of the masses. This means preparing for peaks in demand, while maintaining performance to meet your users’ expectations. Scaling up and scaling out are both important. You can certainly add more processing grunt in a centralized data center, but there comes a point where it becomes more cost- and power-efficient to scale horizontally with a distributed inference model – even more so where latency matters for certain applications, such as: 

• voice assistants that require sub-second response times to enable natural conversation flows,
• autonomous drones/vehicles responding to IoT sensor data, or 
• agentic AI applications that may need to leverage geographically dispersed resources for real-time decision-making, autonomous coordination, and dynamic workload distribution across edge networks. 

This requires thoughtful modularization and portability of your AI application, achieved on Akamai Cloud with our Kubernetes orchestration engine and ecosystem and a platform to simplify and accelerate the deployment of scalable apps. Modularization and portability enable you to scale your AI application and the operations that support it. Kubernetes has become the de facto standard for cloud native computing, making portability far more manageable. 

The chances of having access to the right mix of computing resources wherever the model instance is located drastically improves by embracing open, no lock-in paradigms that promote portability across hybrid- and multi-cloud environments. Containerizing AI with Kubernetes is the approach we have chosen as the foundation for our scaling solutions.

Maintaining Relevance

Like humans subscribing to life-long learning, AI models also need to sharpen their model weights with refreshed data sets, learning from feedback, and refining their context as things change. Continuous training on new data becomes increasingly complex across a distributed model, particularly because coordinating and synchronizing updates across multiple nodes or locations can lead to challenges in maintaining consistency.  

This requires collecting data from the location at which a distributed instance of your AI application/model is deployed, enabled with object storage and vector database solutions to enable retrieval augmented generation (RAG), and a mechanism to shuttle that data back to the central model for retraining or fine-tuning. AI inference on Akamai Cloud is built with strong foundational data management underpinned by key partnerships with leading data fabric platform providers. These core data management capabilities ensure that models can collect performance, domain, and updated data based on current events to provide rich, relevant, and real-time context to the model for more accurate outputs. This also reduces the risk of hallucinations.  Furthermore, this data can then inform the centralized model to help with retraining to adjust model weights for improved relevant inference at a global model scale. 

Akamai Cloud enables you to tackle several inherent challenges with delivering enterprise AI:

• Cost efficiency – while cost is often a driver for selecting a distributed AI inference deployment model by running inference closer to users (see ebook), further cost optimization can be achieved by selecting compute options that offer acceptable performance at affordable rates. At Akamai, we are helping solve this cost conundrum by providing GPUs with well-balanced performance and cost ratios as well as enabling model optimization techniques for commodity CPU inference. 

• Energy consumption and sustainability – AI inference workloads can consume massive amounts of power, with data centers and AI accelerators drawing immense power for executing models. This contributes to global carbon emissions and to organizations’ carbon footprints. As AI adoption scales, the energy demand for AI inference will surpass training, creating further sustainability challenges. Distributing AI inference supports strategies to reduce carbon emissions by reducing data transmission with localized inference, optimizing models for lower-powered processing with selective use of AI accelerators, dynamic scaling of AI applications, and leveraging green power data centers. 

• Federated learning – this refers to the challenge mentioned above: managing the learning rates and evolution of different instances of your AI models dispersed across a distributed cloud environment. It becomes important to adopt a means of keeping versions of your models synchronized with a form of centralized learning oversight. This can entail realigning model weights locally and then synchronizing them across all instances of the model with a federated learning mechanism.

• Securing your models – protecting your AI models from cyberattacks, including novel threats, data leakage, compliance risks, and adversarial attacks is essential for enterprise-grade AI applications to prevent compromising the fidelity or safety of AI models, or disrupting their accessibility altogether. It’s important to secure both inbound AI queries and outbound AI responses with real-time, AI-native threat detection, policy enforcement, and adaptive security measures to defend against prompt injections, sensitive data leaks, adversarial exploits, and AI-specific DoS attacks. Securing models is of paramount importance to enterprises, and while not within the scope of this blog, you can learn more about Akamai’s Firewall for AI here. 

Shaping The Future of AI 

At Akamai, we believe that distributed AI inference is the backbone of scalable, high-performance AI applications. Akamai Cloud is designed with infrastructure to simplify deployment of enterprise AI applications while delivering decision-ready insights at the speed and reliability that your business needs to serve users where they are. Partnering with leading vendors to integrate world-class software into our AI inference stack, Akamai Cloud is designed to solve the challenges of scaling AI and provides the real-time execution environment needed to empower AI agents to orchestrate tasks, optimize workflows, and drive autonomous decision-making at scale. 

Leveraging the right strategies to optimize your AI applications is key to achieving balanced performance, cost, and sustainability while ensuring that they deliver high-fidelity inference.  Feedback loops that constantly assess and improve your models need a well-planned data strategy that serves as the foundation of ongoing learning that keeps your AI application relevant and accurate.  

We’re excited by the AI applications our customers are building on Akamai Cloud today and can’t wait to see what you’ll build tomorrow. 

Interested in learning more about AI inference performance benchmarks? Read our white paper.