The Pros and Cons of AI Use in Your NAS
In recent years, Artificial Intelligence (AI) and Large Language Models (LLMs) have emerged as transformative technologies across various industries. Their ability to process vast amounts of data, automate complex tasks, and provide intuitive user interactions has made them invaluable in applications ranging from customer service to data analysis. Now, these technologies are making their way into the realm of Network Attached Storage (NAS) devices, promising to revolutionize how users store, manage, and interact with their data.
The integration of AI and LLMs into NAS systems is more than just a buzzword—it represents a shift toward smarter, more efficient data management. From improving search and categorization through AI recognition to enabling natural language commands for administrative tasks, the potential applications are vast. However, these advancements also bring challenges, particularly in terms of security and data privacy. In this article, we’ll explore the current state of AI and LLM use in NAS devices, the benefits of local deployment, the security concerns that need addressing, and the brands leading this exciting transformation. Whether you’re a tech enthusiast, a small business owner, or a large enterprise, the rise of AI-powered NAS systems is a development worth understanding.
The Use of AI Recognition in NAS to Date
AI has steadily evolved in NAS systems, but its use has largely focused on recognition tasks rather than broader assistance or intelligence. In its early stages, AI in NAS was synonymous with recognition technology in photo and video management. This included tagging and categorizing images by identifying objects, people, animals, and scenery. These tools offered a way to organize vast amounts of data efficiently but required manual intervention to capitalize on their functionality. While helpful, these recognition tasks were limited in scope and often felt like minor conveniences rather than transformative innovations. They were more about helping users sift through data than empowering them to interact with it dynamically.
Surveillance was another area where AI found its niche in NAS systems. AI-powered surveillance solutions could identify individuals or objects in live video streams, providing real-time alerts and aiding in security operations. However, this application came with significant resource demands, requiring high-performance CPUs, GPUs, and robust storage solutions to process the data effectively. For example, recognizing someone as “David Trent” and verifying their access rights demanded not only live video analysis but also database integration. While advancements in hardware have made these processes less resource-intensive, they still remain confined to niche use cases. The introduction of large language models (LLMs) in NAS systems is set to change this, offering more versatile, interactive, and user-friendly AI capabilities.
How Are NAS Brands Going to Take Advantage of AI?
In we to ignore the clear profitability that integrating AI services will bring to all NAS manufacturers as a means to promote their products, we need to all agree that presenting the clearest, easier and jargon-free means for a user to access their data is paramount. To go on the briefest tangent, lets say we look at the cloud industry and the NAS industry over the last 2 decades – one (cloud) provides convenience, simplicity and low cost of entry, whereas the other (NAS) provides better long term value (TCO), capacity and security control. Up until the last few years, in an increasingly AI-data driven market place, cloud services have been able to leverage AI more affordably to end users thanks to all computations happening at the cloud (i.e remote data center/farm level). However, the market is changing and alongside increased affordably of bare metal server solutions, there is a growing awareness as to the security hazards of your data being on ‘someone elses computer’ and the growing ability for AI powered processes to be made possible partially and/or fully on local bare metal servers on site.
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In short, we are going to see the improved accessibility of data made possible via AI language models that are going to allow non network storage or IT minded professionals to be able to get their data to do WHAT they want, WHEN they want, and HOW they want – securely and safely!
The Security Issues of AI/LLM Use in NAS Systems Right Now
Although the security concerns of AI use and AI access are multi-faceted (which is arguably true of any data-managed or accelerated appliance), the main security implementations stem from unauthorized access. From theft or ransomware, to traditional personal privacy – the bulk of security concerns come down to finding a balance between ease of access, speed of response and security of the process from beginning to end. Encrypted tunnels whereby data is locked from access at the start point and unpacked at the endpoint have been vital in this process, but also rely heavily on a powerful yet efficient host/client hardware relationship to get this done – yet another benefit of powerful local/bare metal servers that allow direct user hardware control. But also tailored security access, control of bare metal systems to create airgaps, custom authentication patterns, selective access locations, and probably most important of all – FULL control of that host/client delivery. The real market demand right now in NAS is for archival and/or hot/live data to spend no time on any other server that is not your own. Encrypted or not, businesses and home users alike are still exceptionally weary of having their corporate/personal confidential data on “someone else’s computer” – at best because they do not want it used as training data for someone else’s AI model, and at worst because they do not want their privacy fundamentally infringed. A local AI that is running in and through a bare metal NAS will lock in access to a single controlled site, with the added benefit of total control of each part of the data transaction.
The integration of AI and LLM services in NAS devices brings numerous opportunities, but it also exposes users to critical security risks. The most prominent concern is the reliance on cloud-based AI platforms like ChatGPT, Google Gemini, and Azure AI. These services require data to be sent to remote servers for processing, which can create vulnerabilities. Organizations handling sensitive or regulated data, such as healthcare providers, law firms, and financial institutions, face significant risks when relying on these external platforms. Even with encrypted transfers and secure API keys, the mere act of sending data offsite can violate privacy regulations and increase exposure to cyber threats.
Another issue is the potential for misuse or unintentional inclusion of user data in external AI training datasets. Even if data isn’t directly accessed by unauthorized parties, it may still contribute to refining and training external AI systems. This lack of transparency creates distrust among users, particularly those who have invested in NAS systems to avoid cloud dependence in the first place. Regulatory environments such as GDPR and HIPAA further complicate the picture, as these frameworks impose stringent requirements on data privacy. For businesses that prioritize confidentiality, these risks underscore the importance of locally deployed AI solutions that keep data within their private networks.
How have recent CPU developments Improved local AI NAS Use?
AI Processes are always going to be heavily constrained to the speed and capability of the system hardware (whether that is the client or the host server) and having access to an enormous database of data from which to subtract information and an AI/LLM to interface with it effectively are just 2 pieces of the puzzle. In many ways alot of this has already been possible for well over a decade. Having efficient yet powerful hardware to do this has only really been conventionally possible in the way we need it thanks to power vs output tradeoffs in modern hardware. The shift in CPU profiling slightly away from traditional cores, threads and clock speeds and now towards accounting for NPU abilities and intelligent CPU/GPU offload has been quick, but vital. Having “the fastest horse in the race” is no longer the be-all-end-all.
| Key Feature | CPU (Central Processing Unit) | NPU (Neural Processing Unit) | GPU (Graphics Processing Unit) |
|---|---|---|---|
| Core Functionality | Versatile processor for managing general computing tasks like running software and handling operating system operations. | Purpose-built for AI and machine learning workloads, specializing in neural network processing and inference tasks. | Primarily designed for parallel processing, focusing on rendering graphics and accelerating computation-heavy operations. |
| Core Design | Few cores optimized for linear processing and multitasking capabilities. | Hundreds to thousands of small cores designed for efficiently handling matrix and tensor operations in deep learning applications. | Built with hundreds to thousands of cores tailored for large-scale parallel computations. |
| Performance Strength | Well-suited for a variety of tasks but less effective for operations requiring massive parallelism. | Delivers exceptional performance for AI inference, training, and related tasks, with low latency and high efficiency. | Excels at high-throughput tasks like graphics rendering, video processing, and AI model training. |
| Primary Applications | Best for everyday computing, including spreadsheets, application management, and operating system processes. | Ideal for AI-related tasks, such as natural language processing, voice recognition, and image analysis. | Perfect for high-demand graphical and parallel workloads like gaming, video editing, and scientific simulations. |
| Power Efficiency | Consumes more energy for tasks outside its intended scope but is generally optimized for standard workloads. | Highly energy-efficient for AI operations, ideal for low-power devices, edge applications, and data centers. | Moderately power-efficient for intensive parallel workloads, optimized for tasks like gaming and rendering. |
| Overall Summary | The CPU is the all-purpose processing unit, excelling in versatility but less specialized for parallel-intensive tasks. | The NPU is the AI-focused unit, optimized for high performance in deep learning and neural network computations. | The GPU is the parallel processing powerhouse, designed for rendering and computationally demanding tasks. |
Now that the benefits of utility of AL/LLM in both home and business are pretty well established, this has resulted in huge development towards processors that seek to find the balancing point between power used and power needed. Intel has been by and large the biggest player in this market and already. The Uktra Core series and the recently launched Xeon 6 processors are pivoting alot of the AI learning and execution away from the heavier and more power-consuming GPU activities (as well as delegating resource use internally as needed when the need arises). The result is that these newer wave of better balanced CPU vs GPU vs NPU processors are going to be significantly better at handling future AI-managed processes, as well as reducing power consumption and shrinking the systems that are managing hundreds of thousands of these operations. Add to that the benefits of localised AI processes requiring bare metal systems with the capabilities to get the job done without cloud resources, and Intel are in a great position right now to dominate this AI processor space.
The Benefits of Local AI/LLM Deployment
Deploying AI and LLM services locally on NAS devices addresses many of the security and compliance concerns associated with cloud-dependent solutions. When AI operates entirely within the confines of a NAS, sensitive data never leaves the user’s controlled environment. This eliminates the risk of unauthorized access, data leaks, or inadvertent inclusion in external AI training. Industries like healthcare, finance, and legal services stand to benefit immensely, as they often handle data that is highly sensitive and subject to strict regulatory standards.
In addition to bolstering privacy, local AI deployment also offers substantial performance advantages. Tasks such as querying databases, generating reports, or categorizing large datasets can be processed faster since they don’t rely on an internet connection. For users in remote locations or those with unreliable internet, this capability ensures consistent performance. Furthermore, local deployment allows for highly customized AI models tailored to specific organizational needs, from managing unique workflows to optimizing resource allocation. By keeping AI processing close to the data source, local deployment combines efficiency, security, and adaptability in a way that cloud solutions cannot match.
The Administrative and Usage Benefits of Local AI/LLM Services on NAS Storage
The integration of local AI/LLM services into NAS systems not only enhances security but also revolutionizes the way users interact with their devices. One of the standout features is the ability to use natural language commands for system management. This eliminates the complexity of navigating intricate menus and understanding technical jargon. For instance, instead of manually adjusting user permissions or configuring backups, users can issue simple commands like “Share this folder with the marketing team” or “Backup all files from this month.” The system interprets these instructions and executes them seamlessly, saving time and reducing frustration.
From an administrative perspective, this functionality is a game-changer. IT professionals can automate repetitive tasks such as user management, system monitoring, and data organization, freeing them to focus on strategic initiatives. For smaller businesses or individual users, this democratization of technology reduces the learning curve, making advanced NAS functionalities accessible to non-technical users. Additionally, local AI systems can analyze usage patterns to optimize system performance, flag potential issues before they escalate, and even suggest improvements. Whether for personal use or enterprise deployments, local AI and LLM services make NAS devices more intuitive and effective tools.
Where are Hard Drives in local AI NAS Use? Are they too slow?
The database that an AI service or infrastructure interfaces with, as well as the core system hardware that supports the LLM/Training model are only going to be as effective as the delivery for the data from point A to point B. Having a super intelligent and well-trained AI connected to a comparative continent of records and information is largely useless if the delivery of the data is being bottlenecked. Until recent years, the answer was thought to be SSDs – with their higher IO and superior simultaneous access abilities. However their own individual shortfall has always been one of capacity – the sheer growth weight of NEW data at any given moment is truly astronomical, and that is not even factoring in the scale of an existing database.
Therefore Hard Drives once again have had to come to the rescue and although the reduced TCO of HDDs, as well as their phenomenal capacity growth, has been hugely appealing – HDD technology has not been sleeping during the AI boom! Intelligent caching, the benefits of multi-drive read/write operations in large scale RAID environments, AI-driven tiered storage alongside strategic SSD deployment – all of this and more have resulted in an AI driven world that, rather than turning it’s back of mechanical hard disks, has actual embraced and increased their utility!
Brands Currently Engaging with Local AI Deployment
The integration of local AI services in NAS systems is no longer a niche feature, with several brands leading the charge in this space. Synology, a long-established player in the NAS market, has developed an AI Admin Console that allows users to integrate third-party LLMs like ChatGPT and Gemini. While this approach relies on external platforms, it offers granular controls to limit data exposure, providing a middle ground for users who want advanced AI features without fully sacrificing security. This hybrid model appeals to users who need both functionality and control.
Zettlabs, a lesser-known yet innovative company, has embraced fully local AI solutions. During a demonstration at IFA Berlin, Zettlabs showcased a crowdfunding project featuring offline AI capabilities. The system processed complex queries using only local datasets, such as querying an eBook database or analyzing medical records without requiring internet access. This approach highlights the potential for offline AI in specialized industries like healthcare and education. UGREEN, a brand known for its DXP systems, is also exploring local AI deployment. Their systems focus on efficient offline processing and interactive management, providing another compelling option for users seeking privacy-first AI solutions. Together, these brands are shaping the future of AI-powered NAS devices by prioritizing user privacy and functionality.
Is AI and LLMs in NAS A Good Thing?
The integration of AI and LLM services into NAS systems is poised to transform how users manage and interact with their data. By automating complex processes, simplifying interfaces, and enhancing overall efficiency, AI-enabled NAS devices are unlocking new possibilities for both personal and professional use. However, the security challenges posed by cloud-reliant AI solutions highlight the critical need for locally deployed systems that prioritize data sovereignty and user control.
As brands like Synology engage with integrating 3rd party cloud AI/LLM services into their collaboration suite, QNAP integrates AI into their systems with modual TPU upgrades that QuTS/QTS can harness, and brand like Zettlabs and UGREEN start rolling out local AI deployment affordably, the market is rapidly evolving to meet the needs of diverse users. These advancements not only address privacy concerns but also open the door to more versatile and intuitive NAS functionalities. Whether through hybrid solutions that offer controlled cloud integration or fully offline systems designed for maximum security, the future of AI-powered NAS is promising. For users willing to embrace this technology, the combination of local AI’s speed, customization, and privacy ensures a more efficient and secure data management experience. As these systems mature, they are set to become indispensable tools in the digital age.
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