Don’t buy a NAS for local AI, get this instead


A NAS is a great addition to any homelab setup. It can back up your photos and other files, run a Jellyfin or Plex server, and replace a few of your subscription services.

It is tempting to think that they’d also be a good way to get into local AI. Unfortunately, their hardware really isn’t up to the job. If you need something compact and energy efficient, there are better, most cost-effective options available.

A NAS isn’t the answer for local AI

They’re convenient but not the best

NAS units are readily available, relatively affordable, and popular, since they can help you cut subscription costs. Increasingly, they’re also being used to self-host some AI applications. However, you shouldn’t buy one specifically for AI.

Most major NAS manufacturers build their units with low-power CPUs meant for serving files without consuming a ton of electricity, not handling AI workloads. Even if you have a high-end unit with 32GB of RAM or more, you’re looking at only a few tokens per second. You’d find yourself waiting on a machine that generates text slower than you can type.

If you already have a NAS, there is certainly no harm in using it for AI—just don’t spend money on one with that purpose in mind.

Quiz
8 Questions · Test Your Knowledge

Unique and creative DIY NAS setups
Trivia challenge

From old laptops to dusty routers — find out how well you know the wild world of homemade network storage.

HardwareNetworkingSoftwareDIY BuildsStorage

Which major advantage makes an old laptop a surprisingly good candidate for a DIY NAS build?

Correct! A laptop’s built-in battery acts like a mini UPS (uninterruptible power supply), protecting your data from sudden power outages. This is a significant perk that desktop-based NAS builds don’t get for free.

Not quite. The big hidden advantage of a laptop NAS is its built-in battery, which functions as a natural UPS. This keeps the system running briefly during power cuts, protecting data integrity without any extra hardware.

Which open-source firmware is most commonly flashed onto compatible routers to enable NAS-like USB storage sharing features?

Correct! OpenWrt is a Linux-based open-source firmware that replaces stock router firmware and adds powerful features, including USB storage sharing via Samba or NFS, turning a basic router into a lightweight NAS.

Not quite. OpenWrt is the go-to open-source firmware for repurposing routers. Once flashed, it supports USB drives connected to the router’s USB port, enabling basic NAS functionality like Samba file sharing on a very small budget.

Which NAS operating system is specifically designed to run well on low-power ARM-based single-board computers like the Raspberry Pi?

Correct! OpenMediaVault (OMV) is a Debian-based NAS OS that supports ARM architectures, making it a popular choice for Raspberry Pi NAS builds. It’s lightweight, free, and has a web-based GUI that simplifies setup.

Not quite. OpenMediaVault is the answer. Unlike TrueNAS or Unraid, OMV is optimized to run on ARM processors, which is why it’s the community favorite for Raspberry Pi-powered NAS projects.

When building a NAS using a Raspberry Pi, what is the most common bottleneck that limits file transfer speeds?

Correct! On older Raspberry Pi models (prior to the Pi 4), both the USB ports and the Ethernet port shared the same USB 2.0 bus, creating a significant bottleneck when transferring data between network and storage simultaneously.

Not quite. The real culprit on older Raspberry Pi models is the shared USB and Ethernet bus. Because both the network adapter and USB storage competed for the same bandwidth, real-world NAS speeds were often far below what the hardware theoretically promised.

What is a ‘Franken-NAS’ commonly referred to in DIY storage communities?

Correct! A ‘Franken-NAS’ is a beloved DIY term for a NAS cobbled together from spare and salvaged parts — old desktop cases, mixed hard drives, and recycled motherboards all stitched together into one functional (if ugly) storage machine.

Not quite. A Franken-NAS refers to a storage build assembled from mismatched, salvaged components — think old desktop parts, second-hand drives, and whatever case happens to fit. It’s a badge of honor in the DIY NAS community.

Which RAID level is recommended for a small 2-drive DIY NAS that prioritizes data redundancy over total storage capacity?

Correct! RAID 1 mirrors data identically across two drives, meaning if one drive fails, your data survives on the other. It cuts your total usable capacity in half but provides simple, reliable redundancy — perfect for a two-drive home NAS.

Not quite. RAID 1 is the right answer for a two-drive redundancy setup. RAID 0 stripes data for speed but has zero redundancy, and RAID 5 or 6 require three or more drives. RAID 1 mirrors your data across both drives for straightforward protection.

What protocol do most DIY NAS builders configure to allow Windows PCs on the local network to browse shared folders like a network drive?

Correct! Samba implements the SMB (Server Message Block) protocol on Linux and Unix systems, enabling seamless file sharing with Windows machines. It’s the standard choice for home NAS builds because Windows natively understands SMB shares.

Not quite. Samba, which uses the SMB protocol, is the standard answer here. It allows Linux-based NAS systems to present their shares in a way Windows PCs understand natively, so you can map them as network drives without any extra client software.

Which low-power x86 platform became extremely popular for DIY NAS and home server builds due to its fanless design and efficient Intel Atom or Celeron processors?

Correct! Compact Chinese-manufactured mini PC boards from brands like Topton and Cwwk, featuring Intel’s N100 or N5105 processors, became hugely popular in the DIY NAS community around 2022–2024. They offer multiple 2.5GbE ports, low power draw, and multiple SATA connections at a very low price.

Not quite. The Topton and Cwwk N100-based mini PC motherboards became a community favorite for budget DIY NAS builds. They pack multiple Ethernet ports, SATA connections, and efficient modern CPUs into a tiny, affordable package that traditional options couldn’t match at the price.

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What matters most for AI

NAS hardware is optimized for things like power consumption and number of drive bays—neither of which really help with AI workloads. You shouldn’t pay too much attention to NPU marketing at this point either. Most of the important AI tools, like Ollama, llama.cpp, and LM Studio, don’t currently send AI workloads to the NPU anyway.


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Note: It may be possible for future software to simultaneously take advantage of an integrated GPU and an NPU.

Memory bandwidth is also a significant limitation, which is why the unified memory approach used by modern Macs gives them an edge over other mini PCs that use RAM sticks.

A Mac Mini is a surprisingly good option

Great performance for all tasks

An M4 Mac Mini sitting on top of a PC case. Credit: Goran Damnjanovic / How-To Geek

If you need something compact and power efficient to run small to medium-sized AI models, a Mac Mini is a pretty good option. The Mac Mini can be equipped with up to 64GB of unified memory for $2000, which allows it to easily run models with 30 billion (30B) parameters. You could probably squeeze in some quantized models with up to 70B parameters, but you’re going to run into some performance bottlenecks.

Mac Mini (M4).

9/10

Storage

256GB

CPU

Apple M4 10-Core

Memory

16GB

Graphics

10-Core M4 GPU

Powered by an impressive M4 chip, the redesigned Mac Mini starts with 16GB RAM, 256GB SSD, a 10-core CPU, and a 10-core GPU.


Since it works out of the box with LM Studio and Ollama, you can easily switch between using it as your day-to-day PC, home server, or dedicated local AI box.

The Mac Mini has one limit

The only real drawback of the Mac Mini is the memory limit. With a 64GB max on the M4 Pro, you won’t be able to run models with more than about 70 billion parameters. If you need to run larger models, you have to jump up to a Mac Studio, which is significantly more expensive.

A mini PC with an AMD AI chip is great too

More memory than you know what to do with

Rear of ACEMAGIC M1 Mini PC. Credit: Bill Loguidice / How-To Geek

However capable Mac Minis are, they’re not the only mini PC that can serve as an at-home AI server. If you’re looking for a reasonable option that costs less than the Mac Mini, start with mini PCs that have an AMD Ryzen AI 9 HX 370.

As one example, MINISFORUM produces a mini PC with a Ryzen AI 9 HX 370 and 32GB, 64GB, or 96GB of RAM that starts around $1,100. Multiple manufacturers produce models with up to 96GB of RAM, though those usually cost around $2,000.

If you need even more power, I’d suggest looking at mini PCs with the Ryzen AI Max+ 395 or the AI Max+ 388. They support up to 128GB of unified memory, and you could allocate up to 96GB as VRAM. This allows you to comfortably run 70B+ models that would be impossible to fit on a Mac Mini.

Unfortunately, mini PCs with the AI Max+ 395 processor and 128GB of RAM are pretty pricey, though they tend to be a bit less expensive than the Mac Studio with an equivalent amount of RAM. When the newer AI Max+ 388 becomes widely available, it’ll likely be a bit cheaper and may be a good option if you’re looking to save some money on an AI PC.

ROCm provides an edge

AMD’s ROCm has matured quite a bit, and it now runs llama.cpp, Ollama, and LM Studio without too many issues. It’s a reasonable choice if you prefer Linux for headless servers.

AMD claims that you could see up to 12.2x faster time-to-first-token than Intel Lunar Lake on some models, the speed is potentially there. As an added perk, if you get a mini PC with OCuLink ports, you have the ability to add a separate GPU later on if you want the extra performance.


You shouldn’t skip on the NAS

Just because a NAS won’t be great for running AI models doesn’t mean they’re not worth anything. They’re great for what they are: convenient storage devices. If you don’t already have an in-home backup solution of some kind, I’d generally recommend buying one.

If you want a device that is both a NAS and an AI server, the most cost-effective option is to build your own from refurbished or secondhand parts. You can add as much RAM as you want, pick a GPU with enough VRAM to run the models you want, and then continually add new drives as your storage needs grow. A home server like that would also be capable of self-hosting almost any other service you want, giving you the option to cut subscriptions in favor of more privacy-friendly alternatives.



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Embodied Intelligence and the Phenomenology of AI explores how human cognition arises from perception, embodiment, and experience in contrast to disembodied artificial intelligence.

Conceptual diagram illustrating embodied intelligence and the phenomenology of AI through perception, embodiment, environment, and experience.

A Conscious Intelligence Perspective

The rapid development of artificial intelligence has transformed modern discussions about cognition and intelligence. Machine learning systems now recognize patterns in data, generate language, analyze images, and assist with complex decision-making processes across scientific, economic, and technological domains. These capabilities have led some observers to suggest that artificial systems may eventually replicate or even surpass human intelligence.

Yet beneath these technological achievements lies a fundamental philosophical question: what does it mean to be intelligent? While artificial intelligence can perform impressive computational tasks, human cognition emerges from a far more complex interaction between perception, embodiment, and lived experience. Understanding this distinction requires examining the concept of embodied intelligence—the idea that human cognition arises through the dynamic interaction between mind, body, and environment.

Phenomenology, the philosophical study of conscious experience, offers a powerful framework for understanding embodied intelligence. Rather than treating cognition as a purely abstract computational process, phenomenology emphasizes that perception, thought, and understanding occur within a lived world shaped by sensory experience and bodily engagement. When applied to contemporary discussions of artificial intelligence, this perspective reveals important differences between human cognition and machine intelligence.

Within the framework of Conscious Intelligence (CI), embodied intelligence highlights the experiential foundations of human awareness and interpretation. It underscores why human cognition remains essential in guiding technological systems, particularly as artificial intelligence continues to expand its capabilities.

Understanding Embodied Intelligence

The concept of embodied intelligence challenges traditional views of cognition that treat the mind as an abstract information-processing system. Early models of artificial intelligence often assumed that intelligence could be replicated through symbolic reasoning and computational logic. According to this perspective, cognition could be understood as the manipulation of symbols according to formal rules.

However, research in cognitive science and philosophy has increasingly shown that human intelligence cannot be separated from bodily experience. Perception, movement, and environmental interaction play fundamental roles in shaping how individuals understand the world (Varela, Thompson, & Rosch, 1991).

Embodied intelligence suggests that cognition arises through continuous engagement between the organism and its environment. Rather than operating as a detached reasoning system, the mind develops within the context of sensory perception and physical action.

Consider a simple example: observing a bird in flight. This experience involves more than visual pattern recognition. The observer’s body subtly adjusts posture, attention tracks motion through space, and prior experiences shape expectations about movement and behavior. The act of perception becomes an integrated process involving vision, spatial awareness, memory, and anticipation.

This dynamic interaction between perception and action forms the basis of embodied cognition. Intelligence emerges not from isolated computation but from the ongoing relationship between body and world.

Phenomenology and the Lived Body

Phenomenology provides a philosophical foundation for understanding embodied intelligence. While early phenomenologists such as Edmund Husserl explored the intentional structure of consciousness, later thinkers emphasized the central role of the body in shaping perception and cognition.

The French philosopher Maurice Merleau-Ponty argued that human consciousness is fundamentally embodied. In his influential work Phenomenology of Perception, he described the body as the primary site through which individuals encounter the world (Merleau-Ponty, 2012). Rather than functioning as an object separate from consciousness, the body becomes the medium through which experience unfolds.

According to Merleau-Ponty, perception is not merely the passive reception of sensory data. Instead, it is an active process in which the body engages with the environment through movement, orientation, and attention. The body provides a framework through which space, time, and meaning become intelligible.

This perspective challenges purely computational models of intelligence. Artificial systems may process visual data or recognize objects in images, but they do not experience the world through a lived body. They do not move within environments, feel spatial relationships, or engage with objects through physical interaction.

Phenomenology therefore highlights a crucial distinction between human cognition and artificial intelligence: human intelligence is grounded in embodied experience, while most AI systems operate within abstract computational environments.

The Limits of Disembodied Artificial Intelligence

Modern artificial intelligence systems excel at tasks involving pattern recognition and data analysis. Deep learning networks can identify faces in images, translate languages, and predict complex trends based on large datasets. These capabilities have created the impression that machine intelligence may soon approximate human cognition.

However, AI systems typically operate in disembodied informational spaces. They process data within computational architectures rather than through physical interaction with the world. Their “perception” consists of numerical representations rather than lived sensory experience.

Philosopher Hubert Dreyfus argued that early AI research underestimated the importance of embodied and contextual knowledge in human cognition (Dreyfus, 1992). Humans navigate the world through intuitive understanding shaped by years of bodily interaction with their environment. Much of this knowledge remains implicit rather than formally articulated.

For example, people can effortlessly grasp objects, maintain balance while walking, or recognize subtle emotional expressions in social interactions. These abilities arise from complex sensorimotor systems that integrate perception and action.

Replicating such capabilities in artificial systems has proven extraordinarily challenging. While robotics research has made significant progress, the embodied adaptability of biological organisms remains difficult to reproduce through purely computational methods.

This limitation suggests that human intelligence involves dimensions of cognition that extend beyond algorithmic processing. Embodied experience provides a context for understanding that cannot easily be reduced to data structures or symbolic reasoning.

Embodiment and Meaning

One of the most important implications of embodied intelligence concerns the nature of meaning. Human understanding emerges through interaction with environments that are experienced through the body.

Language, for example, is deeply connected to embodied experience. Words describing spatial relationships, movement, and sensation reflect how humans encounter the world physically. Even abstract concepts often originate from metaphors grounded in bodily perception.

Artificial intelligence systems can generate language that appears coherent and meaningful, yet they do not experience the embodied contexts that give language its significance. Large language models predict patterns in textual data without possessing an experiential understanding of the concepts they describe.

This distinction helps explain why AI systems sometimes produce outputs that appear plausible yet lack deeper comprehension. Without embodied experience, machines cannot anchor meaning in lived reality.

Phenomenology therefore emphasizes that understanding involves more than symbolic manipulation. Meaning arises from engagement with the world, shaped by perception, movement, and social interaction.

Embodied Intelligence in Human Practice

Embodied intelligence is visible in many aspects of human activity. Artists, athletes, musicians, and craftspeople rely heavily on forms of knowledge that cannot easily be articulated through formal rules. Their expertise develops through repeated interaction between perception and action.

In observational practices such as photography, for example, perception involves more than simply recording visual information. The observer anticipates movement, adjusts bodily orientation, and interprets environmental cues to capture meaningful moments. These processes occur through embodied awareness rather than through explicit calculation.

Scientific inquiry also involves embodied intelligence. Researchers conduct experiments, manipulate instruments, and interpret physical phenomena through sensory engagement with experimental environments. Knowledge emerges through interaction between theory, observation, and experience.

These examples illustrate how intelligence unfolds through embodied practice. Human cognition develops not only through abstract reasoning but also through lived engagement with the world.

Embodied Intelligence and Conscious Intelligence

Within the framework of Conscious Intelligence, embodiment plays a crucial role in shaping how individuals understand and guide technological systems. The CI model emphasizes three pillars—meta-awareness, interpretive agency, and responsible alignment—and embodied intelligence provides experiential grounding for each.

Meta-awareness involves reflecting on one’s own cognitive processes. Phenomenological reflection encourages individuals to examine how perception and bodily engagement influence understanding.

Interpretive agency arises from the human capacity to assign meaning to experiences. Embodied perception provides the contextual richness that allows individuals to interpret information within lived environments.

Responsible alignment involves directing technological capabilities toward ethical and constructive purposes. Embodied awareness can deepen ethical reflection by highlighting the real-world consequences of technological decisions for human experience.

By emphasizing embodiment, the CI framework reinforces the importance of human awareness in guiding artificial intelligence. Machines may extend computational capabilities, but human cognition provides the experiential perspective necessary to interpret and apply technological outputs responsibly.

Toward Embodied Artificial Intelligence

Recognizing the limitations of disembodied AI has led some researchers to explore the possibility of embodied artificial intelligence. Robotics and sensorimotor learning systems attempt to integrate perception and action within physical environments.

These approaches acknowledge that intelligence may require interaction with the world rather than purely abstract computation. Robots equipped with sensors and mobility can learn through environmental feedback, gradually developing adaptive behaviors.

While such research represents an important step toward more flexible AI systems, replicating the complexity of human embodiment remains a significant challenge. Biological organisms possess highly sophisticated sensory systems, neural architectures, and evolutionary adaptations that enable nuanced interactions with their surroundings.

Nevertheless, the exploration of embodied AI highlights an important philosophical insight: intelligence may be inseparable from the environments in which it develops.

Embodied Intelligence in a Technological Civilization

As artificial intelligence becomes increasingly integrated into modern societies, understanding embodied intelligence becomes more important than ever. Digital technologies shape how individuals perceive information, communicate with others, and interact with the world.

Yet human cognition continues to depend on embodied experience. Perception, movement, and sensory engagement remain essential components of understanding.

The rise of AI therefore does not eliminate the importance of human intelligence. Instead, it emphasizes the need for conscious awareness capable of interpreting technological systems within lived contexts.

Embodied intelligence reminds us that cognition is not simply an abstract computational function. It is an activity embedded in perception, experience, and interaction with the world.

Conclusion

The concept of embodied intelligence reveals a fundamental dimension of human cognition often overlooked in discussions of artificial intelligence. While machines excel at processing data and recognizing patterns, human intelligence arises through the dynamic interaction between mind, body, and environment.

Phenomenology provides a philosophical framework for understanding this relationship by examining the structures of lived experience. Through the work of thinkers such as Merleau-Ponty, phenomenology shows that perception and understanding emerge from embodied engagement with the world.

In the age of artificial intelligence, this perspective becomes increasingly relevant. AI systems may extend human analytical capabilities, but they remain fundamentally different from human cognition, which is grounded in embodied experience.

Within the framework of Conscious Intelligence, embodied intelligence underscores the importance of human awareness in guiding technological systems. By integrating reflection, interpretation, and responsibility, individuals can ensure that artificial intelligence serves constructive purposes within human societies.

Ultimately, understanding intelligence requires acknowledging the role of the body in shaping perception and meaning. Human awareness remains rooted in lived experience, and this experiential foundation continues to guide the evolving relationship between human cognition and artificial intelligence.

References

Dreyfus, H. L. (1992). What computers still can’t do: A critique of artificial reason. MIT Press.

Merleau-Ponty, M. (2012). Phenomenology of perception. Routledge. (Original work published 1945)

Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. MIT Press.



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