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One question we often get from clients is—what hardware is needed for AI? That’s a critical question to ask, as the choice of hardware can greatly impact speed, scalability, and cost.
In this article, we’ll break down the essential AI-optimized hardware solutions, from powerful processors to memory and storage requirements, and explain how each piece plays a role in creating a successful AI infrastructure.
Hardware needed for Generative AI includes:
However, you will need a different hardware set for fine-tuning and inference.
Fine-tuning involves training an AI model on specific data with a specific goal in mind. Usually, the models are trained on company data to perform specific AI tasks, but they can also be trained on domain specific data as well.
Inference is a stage at which a trained AI model generates new outputs based on the inputs.
Fine-tuning demands modest computing power with extensive memory and high-speed networking. Inference, on the other hand, focuses on quick and responsive output generation with a reduced need for computing power and an increased need for cost efficiency compared to fine-tuning.
To train AI, you need the following hardware: central processing unit (CPU), graphics processing unit (GPU), RAM, storage, and additional components such as power supply unit (PSU) and cooling.
Training AI models requires extensive computing power because it involves repeatedly adjusting and optimizing the model over many cycles (a process called epochs).
In this process, GPUs and TPUs are very effective because they can process large volumes of data simultaneously, which makes training faster and more efficient.
The AI hardware components can significantly impact the speed, efficiency, and cost of training.
To train AI, we highly recommend considering the following AI hardware options:
CPUs are not as powerful as GPUs for training, but they’re crucial for managing data pipelines, preprocessing, and coordinating AI tasks within the training framework.
GPUs are ideal for AI training because they can handle massive parallel processing and they’re designed to execute thousands of operations simultaneously. The recommended GPUs have high memory capacity and deep learning optimizations.
TPUs are specialized chips designed by Google specifically for machine learning models and deep learning models and tasks. They’re optimized for specific types of matrix operations and provide high performance for training deep learning models. They’re a great alternative to GPUs and are suited for TensorFlow-based models, but are limited for on-premises setup because they’re avaialble through cloud. Hyperscalers such as AWS and Inferentia are competitive hardware, but there’s a difference between TPUs and hyperscaler platforms.
RAM is important as it allows AI models to access and process large datasets, which requires RAM to hold data temporarily and efficiently feed it to the GPUs or TPUs. 128GB is common for larger models, especially GenAI which processes high-dimensional data like text sequences.
AI training often involves handling terabytes of data so fast access is important to avoid slowing down the training process. SSDs offer faster read/write speeds, which reduces the time it takes to load large datasets to memory. They’re often combined with HDDs for cost effectiveness.
You need the following hardware for AI inference: CPUs, GPUs, AI accelerators, field-programmable gate arrays, RAM, storage, and additional components such as PSU and cooling.
While inference doesn’t demand as much computational power as training, it still benefits from fast and efficient parallel processing to generate results in real-time or near real-time.
Therefore, it’s best to rely on optimized CPUs and smaller GPUs if the model is scaled down and optimized as this can help reduce costs and provide a fast response time.
For inference, we highly recommend the following AI hardware options:
Inference doesn’t require the high-end GPUs used for training, but it benefits from optimized GPUs or specialized AI chips that speed up the inference tasks, which is especially needed for complex models.
Recommended smaller GPUs are designed for inference tasks and they offer a cost-effective way to speed up AI applications.
A powerful CPU can handle inference efficiently, for both optimized and not-optimized models. They can handle general-purpose interference tasks, ideal for applications that prioritize cost efficiency. They also work well for lightweight models or when real-time response isn’t essential.
AI Accelerators and FPGAs are specialized AI hardware that can be customized to perform specific AI tasks extremely efficiently. They’re most useful for edge computing and low-power environments. Keep in mind that FPGAs such as Tensor are only available through cloud, not as a standalone hardware.
Inference tasks require quick access to model data so sufficient RAM ensures smooth operations. Most inference setups require only 16GB to 64GB of RAM, depending on the complexity of the model and the number of requests being processed simultaneously.
NVMe SSDs are often used for inference because they offer faster read speeds, which allows models to load quickly when needed.
The key players in the AI hardware space include:
These companies focus on creating specialized processors and infrastructure optimized for the demanding workloads of artificial intelligence.
Yes, AI is expensive to run. Looking at the costs associated with the two main phases of AI deployment (fine-tuning and inference) will help elaborate why.
Fine-tuning requires high-performance GPUs and TPUs to run complex computations over massive datasets.
The costs in this phase are driven by:
On the bright side, fine-tuning costs are mostly a one-time expense for each model, unless further fine-tuning is needed in the future. This means costs can be projected and budgeted, and the process is less-expensive than developing a model from scratch.
Once you go through the initial training process, you typically won’t need to repeat it—at least until your needs change.
Every time an AI system processes an input (like a search query), it uses computational resources.
The costs are primarily driven by:
Tip: Use key performance metrics to track the AI performance.
Large-scale applications with many users require inference across multiple servers or using high-performance GPUs in a distributed setup.
With inference being a continuous process, every user interaction incurs a cost. This can add up significantly over time.
While fine-tuning has a higher upfront cost, the inference-related expenses are lower, but ongoing, which can add up in large scale environments.
AI runs on GPUs because of their superior parallel processing capabilities. However, CPUs can also be used for simpler models and specific and sequential AI tasks.
Some of the best GPUs for AI include NVIDIA models A100, H100, RTX 4090, RTX A6000, L40S, or AMD Radeon RX 7900 XTX.
The number of GPUs depends on the AI model’s complexity, dataset size, and budget, but the low-end for larger models is 8 GPUs while the high-end is 10,000s GPUs.
Are you investing in AI to automate your workflows, save costs, and increase earnings? If you’d like us to help you figure out the right hardware for our AI solutions, please schedule a free 30-minute call with our experts.
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