From AI assistants and content creation to local large language models, here’s how to choose the right memory and storage for different AI workloads.

Artificial intelligence is no longer limited to data centers or research labs. Today, AI has become part of everyday computing. From generating meeting summaries and editing photos to writing code and creating videos, AI-powered applications are changing how people work across industries.

As AI becomes more integrated into daily workflows, many users naturally focus on choosing a more powerful CPU or GPU. While these components remain essential for AI performance, memory (RAM) and storage (SSD) are becoming equally important for ensuring a smooth and efficient user experience.

Whether you’re a content creator, software developer, business user, or system builder planning AI-ready PCs, understanding how RAM and storage affect AI workloads can help you build systems that meet today’s needs while remaining ready for future software developments.

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AI Workloads Are Different from Traditional Computing

Traditional office applications such as word processing, email, spreadsheets, and web browsing typically place moderate demands on a computer’s hardware.

AI applications work differently.

Many AI tasks involve loading large models into memory, processing large datasets, reading and writing temporary files, and saving generated content. These operations require multiple hardware components to work together efficiently.

Bijvoorbeeld:

• AI assistants analyze documents and generate responses.
• AI image generators process large neural network models.
• AI video editing tools render multiple effects simultaneously.
• Local large language models load billions of parameters before inference begins.

As these workloads become more common, memory capacity and storage performance have a greater influence on the overall user experience than they did in traditional office environments.

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Why RAM Matters in AI Workloads

RAM serves as the workspace where AI applications temporarily store models, project files, and active data during processing.

When available memory is insufficient, the operating system begins using storage as virtual memory, which is significantly slower than physical RAM. This can lead to longer loading times, reduced responsiveness, and interrupted workflows.

For AI users, having enough RAM often improves the overall experience more than simply upgrading to a faster processor.

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AI Office Applications

Generative AI has quickly become part of everyday office work.

Applications and services such as:

• Microsoft Copilot
• ChatGPT Desktop
• AI-powered document analysis tools
• AI translation software

typically run alongside browsers, spreadsheets, presentations, and communication platforms.

For these users:

• 16GB RAM provides a comfortable starting point.
• 32GB RAM offers more flexibility for multitasking and larger AI-assisted workflows.

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AI Image Creation

AI image generation has become increasingly popular among designers and marketing professionals.

Applications including:

• Stable Diffusion
• ComfyUI
• Adobe Photoshop with Generative Fill

often require users to keep multiple creative applications open simultaneously.

In these scenarios, 32GB of RAM is generally recommended to ensure smooth operation.

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Local Large Language Models

Running AI models locally is becoming more common for developers, researchers, and privacy-conscious organizations.

Platforms such as:

• Ollama
• LM Studio

allow users to run open-source large language models directly on their PCs.

Depending on model size, these applications can consume tens of gigabytes of memory.

For local AI deployments:

• Small models may run comfortably with 32GB RAM.
• Larger models and more advanced workflows often benefit from 64GB or more.

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Recommended RAM Capacity for Different AI Users

Choosing the right capacity depends not only on the AI software itself but also on how many applications are running simultaneously.

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Why SSDs Matter in AI Workflows

Although AI calculations are handled primarily by the CPU or GPU, storage plays an essential supporting role.

Modern AI workflows involve much more than simply running a model.

Users frequently need to:

• Download AI models.
• Load large datasets.
• Open project files.
• Save generated images or videos.
• Cache temporary data.
• Switch between multiple AI projects.

A faster SSD helps reduce waiting time during these operations and contributes to a smoother workflow.

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Choosing the Right SSD Capacity

AI projects can quickly consume storage space.

Besides the operating system, users must also store AI software, model files, datasets, creative assets, and project outputs.

512 GB

Suitable for:

• AI office assistants
• Basic AI productivity tools
• General business use

1TB

Recommended for most AI users.

Provides enough space for:

• Multiple AI applications
• Creative software
• Local models
• Large working files

2TB or Larger

Ideaal voor:

• Professionele ontwerpers
• AI developers
• Video production
• Enterprise AI deployments

Larger capacities also reduce the need to frequently move or delete project files.

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SATA SSD or NVMe SSD?

Not every AI user requires the fastest available storage.

The appropriate choice depends on workload.

SATA SSD

Still suitable for:

• AI-assisted office work
• Document processing
• Cloud-based AI services
• General business PCs

When AI processing occurs in the cloud, storage speed has a relatively smaller impact.

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NVMe SSD

Recommended for:

• Local AI models
• Image generation
• Videobewerking
• Software development
• Large dataset processing

The higher bandwidth and lower latency of NVMe SSDs improve model loading, project switching, and file handling.

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Storage Form Factor Still Matters

Performance is only one part of the decision.

Physical compatibility is equally important.

Different AI systems may use different storage formats.

Voorbeelden hiervan zijn:

• M.2 2280 NVMe SSD’s for desktop PCs and high-performance workstations.
• M.2 2230 or 2242 SSDs for compact laptops and ultra-small Mini PCs.
• 2,5-inch SATA-SSD’s for business desktops, legacy systems, and industrial computers.

For system builders and OEM customers, selecting the correct form factor ensures compatibility while meeting thermal and space requirements.

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Planning for Future AI Growth

AI software is evolving rapidly.

Models continue to become larger, applications more capable, and datasets more demanding.

Hardware that feels sufficient today may become limiting within a few years.

When planning new systems, it is often worth considering modest upgrades that improve long-term usability.

For many users, increasing memory from 16GB to 32GB or selecting a 1TB SSD instead of 512GB can provide additional flexibility as AI workloads continue to grow.

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Example AI PC Configurations

These examples are intended as general guidance. Actual hardware requirements will vary depending on the AI applications, model sizes, and workloads involved.

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Conclusie

As AI becomes a standard part of everyday computing, selecting the right memory and storage is no longer just about improving performance—it is about creating systems that remain responsive, reliable, and ready for future workloads.

While CPUs and GPUs perform the calculations, RAM provides the workspace AI applications depend on, and SSDs ensure that models, datasets, and projects can be accessed quickly and efficiently. Choosing balanced hardware rather than focusing on a single specification leads to a better overall computing experience.

Op Oreton, we provide memory and storage solutions designed for a wide range of AI-ready systems, from office desktops and Mini PCs to professional workstations and OEM projects. By focusing on reliable performance, broad compatibility, and consistent quality, we help our partners build systems that are prepared for today’s AI applications and tomorrow’s evolving workloads.