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My experience building my own deep learning rig in 2023

My thoughts on building a deep learning rig in 2023: components, costs, performance, value.
Sid Metcalfe

Cartesian Mathematics Foundation


November 21, 2023


I spent a considerable amount of time this year building my own deep learning rig. With respect to GPUs, CPUs, and RAM, I aimed to balance performance with appeal to the gaming market. Searching through secondhand markets for an RTX 3090 was my first challenge. I meticulously researched components considering their role in training models and attracting gamers when reselling. What follows is my experience and rationale behind the choices I made.


Deciding on the Core Components

A photo of the internal layout of a pc highlighting the rtx 3090 24gb gpu and noctua nh-d15 cooler

When building a deep learning rig, choosing the core components is critical for finding a balance between immediate ML workloads and future resale value. For the GPU I settled on the RTX 3090 with 24GB of VRAM. This card is a powerhouse for training complex models, and its hefty VRAM is great for handling large datasets. Securing a secondhand RTX 3090 can significantly cut costs, but due diligence on the unit’s condition is essential.

Finding RAM is the next step. While 32GB suffices for many of my day-to-day tasks, I acquired 64GB (2X32GB) to future-proof my setup. It’s not just about having enough memory; it’s also about the (future) appeal the added RAM has for gamers when I choose to resell.

Storage isn’t as demanding for my use case. I settled on a 1TB NVMe SSD. I could have gone for 500GB but some models nowadays can be pretty large (i.e. hundreds of gigabytes in size).

Regarding the CPU, at first I thought an i5 13600K would be sufficient, but I’ve been swayed by the idea of an i7 for its better performance in multitasking.

For the motherboard (MBD), I’m went with a reliable brand with a moderate price range (it being the X670E Taichi AMD AM5 eATX Motherboard). While tempting to go high-end, the law of diminishing returns kicks in. I considered features like PCIe lanes (e.g. if I’d want a second GPU in the same system in the future), overclocking stability, and potential for future CPU upgrades. I mainly scoured forums and product reviews to make an informed choice.

Cooling is a non-negotiable component, especially when dealing with deep learning tasks that can push a system to its limits. The Noctua NH-D15 comes highly recommended, and I can personally vouch for its performance—quiet, reliable, and powerful. Liquid cooling is another option but it requires more maintenance.

Choosing a case isn’t just about aesthetics for me. Airflow is paramount, so I always learn towards designs that prioritize ventilation while accommodating larger GPUs and cooling solutions. I eyed cases like the Fractal Design Meshify C and the Corsair 4000D Airflow—both are known for their stellar airflow and spacious interiors. I settled on the former (the Fractal Torrent also receives many good reviews).

Every component I selected based on extensive research, community feedback, and compatibility checks, ensuring my build is not only geared for my present ML applications but also holds good resale potential. It can be tempting to cut corners on cost, but investing in key components secures performance and longevity. I believe the drawbacks, such as initial cost and time spent researching, are outweighed by the benefits these choices offer. The deep learning landscape is evolving, and staying adaptable with hardware choices is my strategy for long-term value.

Balancing Performance with Future Resell Value

A bar chart comparing the performance and resell value of components like the i5 13600k and i7 equivalents

As someone building a deep learning rig, the dilemma often boils down to getting the best performance out of the system while ensuring it retains value for future resale, particularly targeting the gaming market. I’ve found that striking that balance requires a tightrope walk between cutting-edge and consumer-friendly components.

Let’s dive straight into the guts of such a build—the GPU. As I said, I zeroed in on an RTX 3090 with 24GB VRAM, not just because it’s a powerhouse for ML tasks, but also due to its gaming prowess. It’s a behemoth capable of deep learning training with hefty datasets, and on the flip side, it crushes triple-A titles at 4K resolution—a win-win for machine learning and gaming resale value.

Selecting the right amount of RAM gets a bit tricky. While 32GB seems enough for my current tasks, opting for 64GB of RAM is about future-proofing and keeping an eye on resale. Gamers and prosumers alike can be quite finicky about specs. Hence, the larger capacity could be a big plus point for resale.

For storage, while my needs hover around the 500GB mark due to reliance on cloud storage solutions for my startup, striking gold in the resale market means hitting that 1TB sweet spot. It’s enough to say, “this rig means business” for potential gaming buyers, without burning a hole in my wallet.

A CPU has to offer the right balance for multitasking and power. The i7 might seem overkill for some, but it’s a sweet spot for heavy lifting in machine learning scenarios and a buzzword that resonates with our gaming friends.

On motherboards, it seems like the dark forest of hardware choices, where you must tread carefully. After much research, it became clear that a robust chipset to support my high-power components without going overboard on gaming-focused embellishments was key.

Noctua’s NH-D15 cooler came up repeatedly in forums and discussions. Its reputation for quiet, efficient cooling aligns perfectly with a rig that’s required to run lengthy computations and, in a gaming scenario, stay cool under pressure during marathon sessions.

Cases can be deeply personal, but what I’ve gathered is that choosing a case is not just about aesthetics; it’s also about functionality—a case that is spacious enough for airflow and future upgrades is vital. I also made sure to pick a case that radiates a ‘serious work’ vibe without alienating the RGB-loving gaming community that might be my future market.

This journey has been a learning curve. There are trade-offs at every turn. For instance, while a beefier CPU may provide incremental benefits for ML tasks, it doesn’t always translate to better gaming performance, potentially affecting resale perception.

Moreover, performance components come with a hefty price, both in upfront costs and power consumption—an aspect not to be taken lightly in the age of environmental consciousness.

Crucially, one thing I’ve learned is that the secondhand market for GPUs is vibrant but volatile. For the uninitiated, there’s a handy guide by the University of California team on Training Massive Deep Neural Networks in a Smart Contract: A New Hope that provides insights into maximizing such a GPU’s potential.

Every component decision has to account for ML requirements today and gaming demands tomorrow. If you’re looking to dig deeper, repositories like TensorFlow over on GitHub offer a wealth of information on compatibility with various hardware setups.

In sum, balancing performance and future resale value is an art and, as I found, an investment strategy—think of it as building a bridge between two worlds. And I’m here, learning to be both an architect and a strategist.

Assembly and Cooling Solutions

Time-lapse photo of a pc being assembled with all the components laid out

Assembling a deep learning rig is much like putting together a complex LEGO set. Each piece needs to fit perfectly for the machine to run smoothly. My primary focus was on ensuring adequate cooling given the power the RTX 3090 GPU exerts - this piece is a beast by all accounts. After having researched extensively, I settled on the Noctua NH-D15 for cooling. It’s a reliable air cooler known for its whisper-quiet operation and exceptional heat dissipation, making it a widely recommended choice among enthusiasts.

Navigating through the motherboard options, I chose one with robust VRM and ample PCIe lanes to complement the high-end GPU. Adequate power delivery is crucial when aiming for top-tier performance, and a good motherboard ensures stability when the system is under heavy computational load while training models.

I was pleasantly surprised by the ease of installation, as the components fitted together effortlessly. However, I ensured that I had enough space in the case for proper airflow, as the RTX 3090 can heat up quickly when under full load. By positioning the fans strategically, I managed to keep the airflow optimal, creating a thermal environment conducive to prolonged deep learning tasks.

The RAM was another critical factor to consider. While 32GB might be sufficient for moderate training models, opting for 64GB can provide headroom for more complex operations – and it’s generally a good selling point for gamers down the line. Additionally, faster memory improves data transfer rates, which is beneficial when you’re crunching large datasets.

For storage, while NVMe SSDs are faster, a 1TB SATA SSD was a cost-effective choice given the budget constraints. It still offers good speed improvements over traditional HDDs for loading data and booting the system. Moreover, if you’re dealing with ample datasets, you may want to consider additional storage solutions or even cloud storage, which can be scaled according to the project needs.

Despite my careful planning, I did face some challenges. Cable management was a bit tedious, and securing the GPU into the PCIe slot required a delicate touch. You want to make certain that the GPU is seated properly to avoid any potential for damage or connection issues.

In terms of drawbacks, while air cooling is effective and economical, a water cooling setup could offer better thermal management for such a powerful GPU. Moreover, the size and weight of the Noctua NH-D15 might pose a problem for smaller cases or more compact component setups.

Throughout the assembly process, I found that having a clear plan and a backup for each component was vital. Ensuring component compatibility and preparing for any mishaps prevented potential delays. For instance, I had a list of alternate RAM kits and SSDs in case my primary choices were out of stock or increased in price.

Overall, the construction phase of a deep learning rig requires meticulous attention to detail and a focus on thermal management. But once assembled, the satisfaction of powering up a highly capable machine built with your own hands is immensely rewarding. This rig is not only a powerhouse for AI and machine learning tasks now but also a prospective high-value asset for future gaming enthusiasts.

Optimizing for Machine Learning vs Gaming Resale

A split-image showing a gaming setup on one side and a machine learning workstation on the other

When building a machine learning rig with an eye towards future resale for gaming, several critical considerations shape both the build process and the choice of components. The primary driver for a deep learning setup is undoubtedly the GPU. My choice of an RTX 3090 is motivated by its colossal 24GB VRAM, vital for training intricate models efficiently. For those interested in this kind of hardware, a quick look at NVIDIA’s deep learning resources explains its suitability for such tasks.

In my experience, while RAM may seem secondary, it’s paramount for handling large datasets essential in machine learning. I opted for 64GB; not only does this benefit my immediate heavy workloads, but from a gaming resale perspective, this sizeable memory is becoming increasingly desirable among enthusiasts, as seen in gaming communities such as r/buildapc.

Regarding storage, I’ve settled on 1TB SSD for expedient data access. Although 500GB might suffice for current projects, the additional space affords me a buffer for unexpected needs and is a checkmark for gamers looking for ample game storage. The speed of an SSD over a traditional hard drive is also a big plus, as fast loading times are a universal preference.

Choosing a CPU presents a balancing act. The i5 13600K serves well for machine learning but opting for the i7 might be beneficial for resale, acknowledging the gaming community’s growing favor for higher-tier processors. A quick research dive on sites like Tom’s Hardware reveals that the i7’s edge in multithreading could be a wise investment.

The motherboard must not be overlooked, as it’s the communication hub for all components – I usually prefer reliable brands known for durable products. Cooling, I believe, is non-negotiable. Given the intense nature of machine learning tasks, a robust cooler like the Noctua NH-D15 is a necessity. It not only maintains the CPU’s longevity but also is a strong selling point for gamers, who can push the thermal limits with overclocking – for reference, check the discussions on

Choosing a case is largely subjective but should not be limited to aesthetics. Adequate airflow is key, and spacious interiors for upgrades make a difference both for ease of assembly now and for potential buyers later who might want to modify or enhance the system.

While focusing on the needs of deep learning tasks, it’s essential to be mindful of potential drawbacks when considering future resale. The high upfront cost of components like the RTX 3090 may not recoup in full at resale, as technology depreciates and newer models emerge. Moreover, preferences in the gaming market can shift; what’s in demand now might not be the case in two years, so resale is a bit of a gamble.

Despite this, building with quality components supports both machine learning prowess and gaming performance. It’s a sound strategy that preserves the value of the build. In my opinion, the key is to strike the right balance between a powerful, future-proof machine learning rig and a desirable gaming behemoth that stands the test of time. And for that, meticulous attention to the selection of components is non-negotiable.

Reflections and Lessons Learned

A thoughtful portrait of a person holding a screwdriver and looking contemplatively at a finished pc build

Building a deep learning rig this year pushed my understanding of machine learning hardware and its intersection with gaming systems. Navigating the specifics of GPUs, CPUs, and cooling taught me important lessons about cost, performance, and forward-planning for resale value. Tapping into the secondhand market was a practical choice, especially for acquiring the RTX 3090, which delivers the essential 24GB VRAM needed for deep learning tasks. Despite the challenges, I’m content with the balance struck between performance and cost.

I took the advice to heart regarding RAM. Opting for 64GB may seem overkill for some setups, but it caters to heavy machine learning models and adds allure for future gaming enthusiasts I plan to sell to. It was a future-proof move, even though initial models run comfortably within 32GB limits. Storage was less critical for my machine learning applications, as datasets could live on external drives, but I nudged it to 1TB for resale appeal.

Selecting the i7 over the i5 for the CPU was a judgment call. While current models I’m working on don’t fully tax the i7’s capabilities, it’s a chip with more perceived value in the gaming community and a wider appeal for resale. The motherboard selection didn’t face such scrutiny, but ensuring it supported my chosen CPU and RAM without bottlenecking the GPU was critical. I settled on one that provided enough PCIe lanes and memory support.

Cooling was one area I didn’t compromise on. After significant research and discussing with peers, I invested in the Noctua NH-D15. It’s reputed for being exceptionally efficient, which means I can run intensive machine learning processes without overheating concerns or throttling.

The case was a simpler decision. It had to accommodate the hefty cooling unit and provide good airflow, but beyond that, aesthetics were secondary. Simplicity won over flashiness, as I believe a clean and well-ventilated case appeals to a broader market.

Among the drawbacks, the sheer power and subsequent energy consumption of the rig was a wake-up call. While necessary for deep learning, it isn’t as eco-friendly or cost-effective for continuous running compared to cloud GPU rental or using more energy-efficient components. Reconciling the up-front investment against potential cloud service costs was a numerical and philosophical exercise. However, the allure of having immediate, hands-on access to my own machine learning powerhouse was persuasive.

In conclusion, the journey has been both rewarding and instructive. I’ve gained practical skills in PC assembly, a deeper respect for the role of individual components, and a better grasp of the nuanced needs of deep learning applications. Navigating trade-offs and looking ahead to maintain resale value was as much a part of the process as the satisfaction of powering up the rig for the first time.

For anyone embarking on similar projects, universities like Stanford provide excellent resources for understanding the demands of machine learning, and GitHub repositories like tensorflow/models are a treasure trove for practical applications. These enlightening sources guided my hardware choices, ensuring my built rig wasn’t just powerful but also relevant to the evolving landscape of deep learning.