Exploring the Expanding Horizons of AI’s Memory: How does this impact the data center?

In the realm of AI sustainability, we’re constantly pushing boundaries.

Google’s recent unveiling of Gemini 1.5 marks a significant milestone with its 1M context window. But what does this mean for the future of AI and our data centers?

Context windows 

Context windows have been the bottleneck. As we witness the evolution of models and transformers, we’re inching closer to a reality where this limitation dissolves. Imagine an AI landscape unshackled from the confines of limited context!

First, let's ground our conversation in what a "context window" really is.

A context window is a limitation imposed on Large Language Models (LLMs) to constrain the amount of input text they can process at a time. This limitation exists for several reasons:

• Computational Efficiency
  Processing long input sequences can be computationally expensive, and LLMs are designed to handle massive amounts of data. By limiting the context window, the model can process input more efficiently and make predictions or generate text faster.
• Preventing Overfitting
  LLMs are prone to overfitting, especially when dealing with long input sequences. A context window helps prevent the model from memorizing the training data and encourages it to generalize better to new, unseen input.
• Improving Generalization
  A context window forces the model to focus on the most relevant information and ignore unnecessary context, improving its ability to generalize to new situations and tasks.
• Reducing Memory Requirements
  By limiting the context window, the model requires less memory to store and process input, making it more feasible to deploy on devices with limited resources.
• Mimicking Human Attention
  The context window can be seen as a mechanism to mimic human attention, where we focus on a specific part of the input and ignore the rest. This helps the model to prioritize important information and make more accurate predictions.

Today, we meticulously craft content-chunking strategies to deal with this constraint, refining them to perfection through a variety of evolving strategies, however, I suspect this investment will be short-lived and yield diminishing returns at an accelerating rate as the context window continues to expand and ultimately be eliminated altogether.

Consider this analogy: Our current AI is akin to a 4-year-old child, learning to read with simple sentences. “The red fox jumps over the log.” As AI matures, like a child’s expanding comprehension, we introduce more complex narratives. “The small red fox jumps over the log to hide. A predator is near, and the red fox is scared. The wolf is hungry.”

As AI grows, so does its ability to grasp and retain context, evolving from simple sentences to intricate stories.

Well, ok.  So how did Google do this and will everyone else do the same? (Definitely.)

Google expanded its Gemini context window to 1M tokens by using a series of deep learning innovations, including:

• Mixture-of-Experts (MoE) architecture: Models are divided into smaller "expert" neural networks, which are selectively activated based on the input given. This makes the model more efficient and allows for longer context windows.
• Sparsely-Gated MoE: An early adopter and pioneer of the MoE technique for deep learning, which helps to enhance efficiency.
• GShard-Transformer: A model that uses the MoE technique to process long input sequences efficiently.
• Switch-Transformer: A model that uses the MoE technique to selectively activate expert pathways in its neural network.
• M4: A model that uses the MoE technique to enhance efficiency and allow for longer context windows.
• Long-context understanding: A breakthrough experimental feature that allows the model to process up to 1 million tokens consistently.
• Efficient transformer: A technique that helps to enhance efficiency and allow for longer context windows.

The Impact on Data Centers

With larger context windows comes the need for more robust data processing capabilities. Data centers must adapt to handle the increased memory and storage performance requirements.

Challenges and Solutions:

• Computational Demand: The full 1 million token context window is computationally intensive, requiring further optimizations to improve latency.  We also need to consider the architecture that responds to these queries and ensure that they are highly resilient and available in the event of failure.  Imagine AI being used in a medical setting.  At best, we delay patient consult time and screw up the metrics for Patients: Doctor, at worst -- someone dies.  This is where Kubernetes comes in.
• Storage Requirements: The model's ability to process vast amounts of information in one go (e.g., 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code, or over 700,000 words) necessitates increased storage capacity at an on-demand rate. 

The velocity at which data is being ingested and new data created means that almost any capacity planning models or practices are no longer relevant.  I know for a fact when I did these exercises as a VP of IT at Credit Acceptance, I foolishly made a bet with our VP of Finance that this would be the last time I asked her this year to buy storage.  She laughed and I lost my bet a few weeks later.  Thank God for Evergreen//One by #PureStorage.  This just isn't a topic anyone has to deal with anymore.

Additional storage requirements would be around availability.  Storage MUST be highly resilient and self-healing in nature.  Regardless of whether you run local nVME or external storage, it must have these capabilities.  I'm certain you're running your AI pipeline on a modern bare-metal Kubernetes architecture and the native CSI's do not meet the mark, nor does your NFS storage.   You need highly performant, and resilient PV's that are capable of self-healing and bringing replicas back online almost immediately.  This is where #Portworx by #PureStorage comes in.  Google also has regionally persistent disks you can use to achieve these results as well, but frankly not as well as Portworx.

With Pure Storage's 1watt<TB efficiency (See my previous article), Pure makes room in the data center for AI.

• Scalability: The model's efficiency, achieved through the Mixture-of-Experts (MoE) architecture, enables scaling across various tasks and data center infrastructure.
• Optimization: Ongoing efforts focus on improving latency, reducing computational requirements, and enhancing the user experience as the full 1 million token context window is rolled out.

Real-World Application in Healthcare: Consider the healthcare industry, where AI could revolutionize the analysis of echocardiogram data. (Something very personal to me.) The backend infrastructure must be capable of supporting high bandwidth requirements to allow AI assistants to alert medical professionals in real time. This is where Pure Storage’s all-flash arrays shine, handling the velocity and volume of data with unparalleled efficiency, and simplicity with the best TCO.

These technological leaps that seem to come weekly at this point create havoc for infrastructure leaders, data center planners, CIO's, CFO's, and on and on.  Their instinct is to sit still and let the noise die down, but this state of paralysis will only defer value and potentially cost shareholders real money.

As we continue to push the boundaries of AI, the following questions come to mind:

• How can we optimize data center architecture to support the growing demands of AI workloads?
• What role will high-performance storage play in unlocking the full potential of AI?
• How can we ensure seamless scalability and management of AI applications in Kubernetes infrastructure?

Share your thoughts and insights on the future of AI and its impact on data center infrastructure.

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