This guide explains what GPUs are, how they differ from traditional processors, why they've become central to AI infrastructure, and what storage and networking requirements GPU deployments create at scale.
A graphics processing unit (GPU) is an electronic circuit designed to perform mathematical calculations at high speed. Originally built to accelerate image and video rendering, GPUs excel at applying the same operation to many data values at once—a capability that has made them essential for machine learning and AI workloads.
Think of a GPU as a specialized calculator with thousands of smaller processing units working in parallel. While a traditional processor handles tasks one after another, a GPU breaks problems into pieces and solves many pieces simultaneously. This parallel processing architecture is what makes GPUs powerful for specific types of work.
GPUs also include dedicated memory—typically GDDR6 or GDDR6X—that stores code and data for compute-intensive operations. This on-board memory lets GPUs access information quickly without competing for system resources, further accelerating processing for demanding workloads.
The key difference comes down to design philosophy. CPUs feature fewer cores optimized for sequential processing and general-purpose tasks like system control, I/O operations, and multitasking. They're built to execute complex instructions in order and manage overall system operations.
GPUs take a different approach. They sacrifice versatility for specialized parallel processing power, using hundreds or thousands of cores to execute the same mathematical operation across vast amounts of data simultaneously. This makes them exceptionally well-suited for graphics rendering, video processing, and the matrix operations that underpin modern AI.
GPUs have become foundational infrastructure for AI because their architecture directly addresses how AI models actually work.
AI and machine learning involve performing the same mathematical operations across enormous datasets. Training a neural network means calculating gradients and updating millions or billions of parameters repeatedly. These operations are highly parallelizable—they can be broken into many independent calculations that run at the same time.
This is where GPUs deliver their advantage. Their architecture processes thousands of calculations concurrently, dramatically reducing training time. Major AI frameworks like TensorFlow and PyTorch are built to leverage GPU acceleration, automatically distributing computations across available GPU cores.
GPUs accelerate both training—where models learn patterns from data—and inference—where trained models make predictions on new data. During training, GPUs handle the mathematical work required to adjust model parameters based on training data. During inference, they enable real-time predictions by quickly processing input through the trained model.
Understanding GPU performance requires looking at both architecture and the types of operations they're designed to handle.
A GPU's architecture centers on its large number of processing cores. While CPUs use fewer cores optimized for complex operations, modern GPUs deploy thousands of simpler cores designed to execute the same instruction across different data points rather than handle complex branching logic.
The GPU distributes instructions across its many cores, with each core executing the same operation on different data points. For workloads that fit this pattern—like the matrix multiplications common in AI—the approach delivers substantial performance gains.
GPUs excel at floating-point operations—the decimal-based calculations essential for graphics, scientific computing, and machine learning. They're particularly efficient at matrix multiplication, the fundamental operation in neural network computations. Modern GPUs also feature high-bandwidth memory that supplies data to processing cores quickly enough to keep them busy.
While both are processors, GPUs and CPUs are optimized for fundamentally different work.
The architectural differences between CPUs and GPUs reflect their intended purposes:
CPUs remain the better choice for tasks requiring complex logic, frequent branching, and low-latency responses to varied operations. They excel at operating system functions, database queries with complex joins, and applications with unpredictable execution paths.
GPUs dominate workloads involving repetitive calculations across large datasets. They're ideal for AI model training, scientific simulations, video rendering, and data-parallel analytics. The key question is whether your workload can be broken into many independent, similar operations—if so, GPUs will likely deliver superior performance.
GPUs have transformed how enterprises approach AI and data-intensive computing across multiple domains.
Training deep learning models represents one of the most computationally demanding tasks in enterprise IT. Models with billions of parameters require processing massive datasets through multiple training iterations. Distributed training frameworks allow organizations to spread training across multiple GPUs or even multiple servers, further accelerating the process.
Beyond training, GPUs enable real-time analytics on streaming data. Financial services firms use GPU-accelerated analytics for fraud detection and risk assessment. Healthcare organizations apply GPUs to medical image analysis. E-commerce platforms leverage GPUs for real-time recommendation engines that process user behavior data instantly.
While AI has become a dominant GPU use case, graphics and visualization remain important. Scientific research relies on GPU-powered visualization to explore complex datasets. Engineering firms use GPUs for real-time 3D modeling and simulation. Media companies depend on GPUs for video editing, effects rendering, and content creation workflows.
Deploying GPUs effectively requires careful attention to supporting infrastructure—particularly storage and networking.
GPUs process data extraordinarily quickly. A modern GPU can have memory bandwidth exceeding 2 TB/second, allowing it to consume training data at rates that easily overwhelm traditional storage systems. When storage can't supply data fast enough, GPUs sit idle—a problem known as GPU starvation.
Storage systems supporting GPU workloads need to deliver consistently high throughput. For AI training workloads involving large datasets, storage needs to sustain substantial bandwidth to keep GPUs fed with data.
Network infrastructure becomes equally critical in distributed GPU environments. When training spans multiple GPUs or nodes, the network handles both high-bandwidth data transfer and low-latency communication between GPUs. The entire data pipeline—from storage through networking to GPU memory—needs optimization as a system.
Techniques like Remote Direct Memory Access (RDMA) help by allowing direct memory-to-memory data transfer that bypasses the CPU and operating system overhead. This reduces latency and frees CPU resources for other tasks.
As GPU performance has increased with each generation, the gap between GPU processing speed and storage performance has widened.
Consider GPU evolution from one generation to the next. Each generation brings substantial increases in processing power, memory capacity, and memory bandwidth. However, these improvements only translate to faster training times if the storage system can supply data at matching rates.
When storage becomes the bottleneck, training time becomes dominated by I/O wait rather than computation. GPU utilization drops, and organizations fail to realize the return on their GPU investment. Fast, scalable storage has become a fundamental requirement for GPU-based AI infrastructure.
Modern AI workloads demand storage that combines several characteristics. Scalability is essential—AI datasets routinely reach petabyte scale and continue growing. S3 compatibility provides a standard interface that integrates with AI frameworks and MLOps tools. High throughput ensures GPUs receive data without delays.
High-performance object storage systems designed for AI workloads—such as MinIO AIStor with NVMe drives and parallel access optimization—can deliver the throughput GPU infrastructure requires. Technologies like RDMA enable direct memory-to-memory data transfer between storage and GPU systems, minimizing latency and maximizing throughput while maintaining the data durability enterprise AI infrastructure demands.
Organizations building AI infrastructure increasingly recognize that storage performance directly impacts GPU utilization and, ultimately, the speed at which they can develop and deploy AI models. Investing in storage systems designed specifically for AI workloads—with the throughput, scalability, and low latency GPUs demand—has become as critical as the GPU investment itself.
Ready to build AI infrastructure that keeps your GPUs running at full capacity? Explore MinIO AIStor, the high-performance, S3-compatible object storage platform designed for exascale AI workloads with the throughput and scalability your GPU infrastructure demands.
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