AI is booming. Globally, a total of $33.9 billion was privately invested in generative AI in 2024. At the same time, 78% of organizations are using AI in their business operations.
This level of growth is supported by numerous technologies, with data centers and machine learning models being the most widely discussed. However, AI can’t accomplish much without the right hardware. From CPUs to RAM to system architecture, hardware arguably plays the most pivotal role in making AI a reality.
Learn why the right hardware is essential to AI computing here.
The Role of Hardware in AI Computing
When people say “AI is moving fast,” they usually mean models and algorithms. In practice, hardware sets the pace. Aspects like compute throughput, memory bandwidth, interconnect topology, and power and cooling determine how big your models can be, how quickly you can iterate, and whether you can meet service-level objectives without going over budget.
Traditional enterprise workloads are CPU‑centric and tolerant of modest latency. AI is the opposite. With AI, training and inference pipelines are largely parallel- and bandwidth-sensitive across the memory and I/O stack — HBM, GPU interconnect topology, PCIe, storage and network throughput, tail latency, and compiler or runtime maturity. Many use cases, such as agentic assistants, also need near-real-time responses. These applications must hit tight latency percentiles and sustained throughput.
As AI demands have risen, specialized AI acceleration hardware is a requirement. Alongside CPUs, GPUs now lead parallel tensor compute. For specific workloads and scale, vendors also deploy ASICs and custom NPUs optimized for AI kernels and memory movement.
NVIDIA Blackwell GPUs, Intel Gaudi 3, Google TPUs and Meta MTIA aim to deliver higher performance per watt and lower cost per token via faster interconnects and smarter memory. Meanwhile, edge NPUs run inference on devices to cut latency, reduce cloud traffic and keep data local, as cloud and data center accelerators handle large‑scale training and high‑throughput services.
AI Computing Hardware Requirements
The most essential hardware requirements for AI computing include processing power, GPU acceleration, memory and storage, and system architecture. Each of these components is essential for efficient and effective AI workflows.
Processing Power
Advanced CPUs coordinate the pipeline, including data loading and augmentation, tokenization, host and device transfers, and orchestration. Here, it’s important to prioritize core count, memory channels, and PCIe lane count and generation availability. NUMA‑aware placement means the right socket feeds the right accelerators. For RAG vector search and feature stores, ample system RAM can be as important as GPU memory.
GPU Acceleration
GPUs deliver massively parallel tensor math for training and high‑throughput inference, with mixed precision to boost throughput per watt. They significantly enhance the speed of computationally intensive tasks such as image processing and computer vision, which helps accelerate AI workflows. For example, NVIDIA’s RTX A4000 serves as an entry‑level accelerator for development, lightweight training, GPU‑accelerated preprocessing, and running smaller CV models or quantized or distilled LLMs.
Memory and Storage
Treat memory and storage as tiers. HBM on the GPU determines whether the model fits, as well as feasible batch sizes and context length. System RAM buffers datasets and powers preprocessing — for RAG workloads, it also drives vector index performance. Storage must deliver high throughput with low tail latency, so NVMe is used for hot data, and caching layers are added to keep accelerators busy. Object storage and the network must be sized so sustained I/O never starves compute.
System Architecture
How you put the system together determines the speed you actually get. Use the right connections and make sure no link is overloaded. Engineers should also plan for power density and cooling, which increasingly means liquid cooling at higher rack loads. The hardware should be paired with optimized runtimes and compilers, kernel fusion, quantization and smart batching. Thoughtful hardware and software co-design can increase effective throughput without adding accelerators.
Consequences of Inadequate Hardware
Using improper AI hardware can have many negative effects:
- Missed business insights: When models can’t fit the required context window, retrieval quality drops. When video can’t be processed at frame rate, detectors miss events. Additionally, underpowered memory or weak interconnects force smaller batches and shorter contexts, degrading accuracy and user experience, no matter how advanced the model architecture is.
- Higher operational costs: If storage or networking can’t feed data to the GPUs quickly, expensive accelerators sit idle. That idle time drives up cost per 1M tokens and per‑inference cost because you’re paying for hardware and electricity without getting results. Inefficient power and cooling push bills even higher, and at larger scales, losses can quickly multiply.
- Competitive disadvantage: Slow training and inference cycles delay product updates and make it hard to keep up with new model releases. Without enough HBM, fast GPU‑to‑GPU interconnects or sufficient network and storage throughput, you can’t scale context length or move up to larger models, meaning accuracy and latency trail fall behind competitors.
Choosing Hardware for AI Computing
Selecting the proper hardware for your AI needs starts with workload evaluation. Understanding the specific demands of your AI applications is crucial for making informed hardware choices. Your use cases will drive your decision.
How much data do you store and move daily? Consider the peak versus average. For example, if you’re looking into video solutions, evaluate resolution, frames per second and codec, which drive I/O and decode needs. Then, assess processing complexity. What does each request cost? Track model size, precision, context and sequence length, batch size, and latency targets. Plan for multi‑GPU and fast links if you find that a model or context doesn’t fit on one GPU.
Over time, your business will need to plan for scaling and expanding its AI implementations. Choose hardware solutions that offer flexibility, such as modular systems and architecture, like modular nodes you can add gradually. For GPU memory, network and storage speed, leave enough free space to anticipate future growth. Moving certain workflows to the cloud can also be highly valuable.
Learn How BCD’s Solutions Support AI Innovation
The value of working with AI is only expected to rise, and organizations everywhere can gain a competitive edge by investing in advanced computing hardware solutions. At BCD, we’ve been supporting AI and machine learning computing with customized hardware solutions for years.
Since we partner with pioneers in the industry, from NVIDIA to Intel, our hardware solutions can integrate seamlessly into your applications. Specifically, our purpose-built hardware can help you with AI-powered video analytics for security deployments.
Whether you’re considering AI solutions or have already jumped headfirst into AI computing, BCD is your go-to partner for advanced hardware. Learn more about our experience and contact us today to get started.
