Would you call a plumber if you have a cold? Would you take your car to the emergency room if the check engine light was on? Would you ask a piano teacher to help you get in shape and teach you yoga?
We’re used to choosing human specialists and experts to help us solve our problems and achieve our goals.
The need for specialization, along with flexibility and adaptability, has never been greater. Like choosing the right expert human, choosing the right AI model, and as important, the right processors, GPUs, and supporting memory for the right workloads is essential.
Amazon’s CEO Andy Jassy knows this. He said:
"The reality is that all of you are going to use different models for different reasons at different times … which by the way is the way the real world works … and human beings don’t go to one human being for expertise in every single area, you have different human beings that are great at different things … You’re gonna sometimes be optimizing for coding, sometimes for math, sometimes for integration with RAG, sometimes for agentic needs, sometimes for lower latency, sometimes for cost …. Most of the time for some combination of these."
No single GPU or processor is perfect for every scenario. With new models being released daily, supporting a particular model, and the ones to come, along with your everyday requirements, requires infrastructure flexibility — an ability to choose and deploy the right tools for specific workloads.
With advances in AI/ML and HPC, you will increasingly leverage different processors, GPUs, and specialized hardware to optimize performance as well as cost efficiency. For AI/ML practitioners, you have to strategically select processors and GPUs to handle workloads that vary widely in their computational needs. This is especially relevant as models become more complex, diverse, and domain specific.
At Liqid, we couldn’t agree more. And in our world, we would have your expert humans –doctor, piano teacher, yoga instructor, and plumber all under one roof. But in the case of our expertise in software-defined composable infrastructure, we’ll help you leverage your GPUs, FPGAs, TPUs, storage, and other specialized processors in the same chassis, so you can apply their unique capabilities where you need them most.
Composable infrastructure lets you design your systems so you can configure and reconfigure components such as compute, storage, and networking resources on demand. Unlike monolithic architectures where resources are statically allocated, composable infrastructure enables businesses to dynamically allocate and optimize resources based on specific workloads and goals — ensuring you can:
- Dynamically match resources to workloads today and in the future as AI and other technologies evolve.
- Optimize hardware utilization – maximize investments and limit waste and tech debt.
- Lower operational costs associated with power, cooling and space while improving performance.
That’s software-defined composable infrastructure. That’s Liqid.
Expertise and specialization
The comparison of hardware specialization to human expertise is particularly apt even in your organization. Just as you rely on different individuals for different skills — a data scientist for ML optimization, an engineer for system architecture, or a financial analyst for modeling — infrastructure should similarly reflect specialization.
Humans don’t excel at everything, and neither do processors. Relying solely on a single processor architecture to handle all workloads is like expecting one person to be an expert in an instrument, mathematics, programming, linguistics, and economics. While possible, it’s rarely efficient.
For instance:
- GPUs are excellent at accelerating neural network training because of their parallel processing capabilities, but they are overkill for simple coding or low-complexity tasks.
- CPUs remain versatile and cost-effective for general-purpose workloads but lack the throughput of GPUs for AI inferencing.
- Newer processors like TPUs or FPGAs fill specialized niches where neither CPUs nor GPUs excel.
This specialization creates an opportunity for you to build composable infrastructure that aligns with the diversity of your workloads.
Why does composable infrastructure matter?
AI is no longer one-size-fits-all. Consider how AI models themselves have evolved. Some models require exceptional parallel processing capabilities to train neural networks efficiently, while others need minimal latency for real-time inference. Similarly, some workloads prioritize mathematical computations, while others focus on integrating with external systems or managing data retrieval pipelines. The reality, as Mr. Jassy points out, is that organizations are constantly optimizing for different factors:
- Coding Efficiency: Some processors excel at supporting development environments, debugging, and iterative coding workflows.
» Ideal hardware: General-purpose CPUs with strong single-threaded performance and large caches.
- Mathematical Computations: AI and ML models often demand hardware that can handle matrix operations, floating-point calculations, and parallel tasks efficiently.
» Ideal hardware: GPUs or tensor processing units (TPUs) built specifically for high-throughput computing.
- Integration with Retrieval-Augmented Generation (RAG): Applications that combine large language models (LLMs) with real-time information retrieval systems require a balance of computation, memory access, and latency.
» Ideal hardware: CPUs, GPUs, and memory optimized for latency-sensitive workloads.
- Agentic Needs: AI agents that interact dynamically with environments (e.g., simulations, robotics, or real-time decision systems) require a mix of compute power, responsiveness, and multi-threaded capabilities.
» Ideal hardware: Specialized GPUs and memory tailored for automated, agent-based tasks.
- Lower Latency: Real-time applications like conversational AI, autonomous vehicles, and fraud detection systems demand ultra-low latency for critical decision-making.
» Ideal hardware: Edge processors, FPGAs (field-programmable gate arrays), or latency-optimized accelerators.
- Cost Optimization: Not every workload justifies the expense of high-performance GPUs. Many smaller or periodic tasks can be executed on commodity hardware to control costs.
» Ideal hardware: Scalable CPUs, cheaper GPUs, or cloud-based spot instances.
Most businesses juggle multiple, often conflicting, priorities. Composable infrastructure addresses these varying demands by dynamically allocating the right resources via software in real time.
The benefits of a composable infrastructure are clear
- Flexibility Across Use Cases: Liqid composable infrastructure enables organizations to shift seamlessly between different workloads. For instance:
- During AI inferencing, allocate high-performance GPUs or TPUs.
- For cost-sensitive batch jobs, use commodity CPUs.
- Cost Efficiency: Optimizing resources reduces waste. Instead of over-provisioning expensive hardware for every workload, Liqid lets you allocate resources on demand, minimizing costs while maintaining performance.
- Scalability: As workloads evolve, Liqid composable infrastructure makes it easier to scale specific components without overhauling the entire system.
- Improved Performance: By matching the right hardware to the right workload, you achieve better performance metrics across tasks such as:
- Reduced inference latency.
- Lower overall energy consumption and space requirements, essential for edge deployments.
Composable infrastructure in the cloud vs on premises
Cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) have offered a T-shirt size version of composability for some time:
- AWS allows users to choose between various instances optimized for compute (C-series), memory (M-series), or GPU (G-series).
- Azure provides specialized hardware like FPGAs for ultra-low latency workloads.
- Google Cloud offers TPUs tailored for deep learning training and inference.
With Liqid, you can have the flexibility to meet your business’ precise infrastructure needs, whether optimizing for performance, latency, or cost, without having to send your sensitive data out to the cloud, and without the expense that comes with cloud services.
A modern infrastructure landscape mirrors human expertise: diverse, specialized, and adaptable.
Specialization solves problems and helps achieve goals. This is true for experts and it’s also true for infrastructure. By embracing composable infrastructure and choosing the right processors, GPUs, and memory for specific workloads, you can unlock new levels of flexibility, performance, and cost savings.
Let Liqid show you how we can give you the specialized, software-defined composable infrastructure you need to dynamically respond to the ever-changing demands of AI, machine learning, and high-performance computing, in addition to everyday tasks and be future proofed for what’s to come.
