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Solutions for AI Development and Deployment

Q: How much VRAM do I actually need?

VRAM (Video RAM) is the hard limit for model size. 32GB (e.g., NVIDIA GeForce RTX™ 5090): The new standard for \"Prosumer\" generative AI and development. Excellent for running quantized 13B-30B parameter models and heavy Stable Diffusion workflows. 48GB - 96GB (e.g., NVIDIA RTX™ 6000 Ada / RTX PRO™ 6000 Blackwell): Required for professional training and unquantized medium-to-large models. 192GB+ (Multi-GPU): Necessary for training large foundation models or running massive LLMs locally. A dual RTX PRO 6000 Blackwell setup provides nearly 200GB of unified memory.

Q: Do I need a rackmount server, or can I use a tower workstation?

Develop (Tower Workstations): Perfect for office environments. Our towers are acoustically engineered to run up to dual RTX 5090s or quad RTX PRO cards quietly enough to sit beside your desk. Scale (Rackmount Servers): Essential for dedicated server rooms. These systems prioritize maximum airflow and compute density (supporting 4-8 GPUs) and are designed for 24/7 uptime in a datacenter.

Q: Do you support Linux for AI development?

Yes! We offer full support for Ubuntu and other major Linux distributions. Our Labs team verifies these configurations with the latest NVIDIA drivers , CUDA , and container toolkits , ensuring you can skip the setup headache and start coding immediately.

Q: Why build vs. rent (AWS/Azure)?

Cloud is great for \"bursting,\" but owning hardware wins on: Cost: Renting the equivalent of a 4x GPU node in the cloud can exceed the purchase price of a Puget workstation in just 4-6 months of heavy use. Data Sovereignty: Keep sensitive proprietary data (finance, healthcare, IP) on local hardware, physically disconnected from public cloud providers. Iteration Speed: Developing locally removes upload/download latency, allowing for faster experimentation cycles—especially when working with terabyte-scale datasets.

Each stage of developing AI models and agents requires progressively more computing power, and here at Puget Systems we have solutions for you every step of the way!

Talk to an Expert

Select YourAI Deployment Stage

No matter which step of the AI development process you are in, we have systems designed to help you now as well as in your next phase of deployment!

Develop

For individuals or small teams building and iterating on models at their desk.

Maximum deskside performance for rapid, local model tuning and experimentation.
Support for various high-end GPUs in a quiet, office-friendly PC or multiple GPUs in a larger tower chassis.
Ideal for data preparation, code development, initial prototyping, and more.

Deploy

For teams moving from local development to a centralized, shareable resource.

Bridge the gap between desk-side development and a full-scale data center.
Validate and test models on production-grade hardware before wide deployment.
Perfect for serving a small group of users or running initial inference applications.

Scale

For organizations implementing large-scale, mission-critical AI infrastructure.

Achieve maximum compute density with enterprise-grade, high-performance GPU servers.
Engineered for 24/7 reliability and seamless integration into existing data center environments.
Power your entire organization’s AI strategy, from training the largest models to high-throughput inference.

If you have questions about what type of computer hardware your specific situation needs, our expert consultants are available to provide individualized guidance or a quote for a custom AI workstation or server.

Our Customers Include

View more of our customers here.

High-Performance Workstations and Servers for Developing, Piloting, Deploying, and Scaling AI Systems

At Puget Systems, our workstation PCs and servers for AI development and deployment are crafted through a combination of our Puget Labs team’s expertise, benchmark testing, customer feedback, and the knowledge our consulting team has accumulated over the years.

Our goal is to make purchasing and owning computers a pleasure, not a hindrance to your work. We are here to help you throughout the process of developing your AI solutions, piloting them internally, deploying them across your team, and eventually scaling them out to your whole organization.

Workstation with Monitor Running AI Demo

Talk to an Expert

We specialize in building workstation PCs, servers, and storage systems tailored for each of our customers. The best way we’ve found to accomplish that is to speak with you directly. There is no cost or obligation, and our no-pressure, non-commissioned consultants are experts at configuring a computer that will meet your specific needs. They are happy to discuss a quote you have already saved or guide you through each step of the process by asking a few questions about how you’ll be using your computer. There are several ways to start a conversation with us, so please pick what works best for you:

Sales Consultation

Tech Support

If you’d rather not wait, you can reach out to us via phone during our business hours.

Monday – Friday | 7am – 5pm (Pacific)

425-458-0273 | 1-888-784-3872

AI Workstations and Servers FAQ

Hardware & Performance

Should I prioritize the CPU or GPU for AI and Machine Learning workloads?

For most AI tasks, such as training deep learning models and LLM inference, the GPU is the primary workhorse. Prioritize your budget towards the most powerful GPU(s) with the most VRAM you can afford.

Don’t neglect the CPU, though! While the GPU does the math, the CPU is the “traffic controller” for data preprocessing and managing multi-GPU configurations – and can become a bottleneck. We recommend AMD Threadripper™ PRO 9000 or Intel® Xeon® W-3500 processors because they provide the massive PCIe lane counts needed to feed 3-4 high-end GPUs without bottlenecks—workloads that standard consumer CPUs are not optimized to handle.

How much VRAM do I actually need?

VRAM (Video RAM) is the hard limit for model size.

32GB (e.g., NVIDIA GeForce RTX™ 5090): The new standard for “Prosumer” generative AI and development. Excellent for running quantized 13B-30B parameter models and heavy Stable Diffusion workflows.
48GB – 96GB (e.g., NVIDIA RTX™ 6000 Ada / RTX PRO™ 6000 Blackwell): Required for professional training and unquantized medium-to-large models.
192GB+ (Multi-GPU): Necessary for training large foundation models or running massive LLMs locally. A dual RTX PRO 6000 Blackwell setup provides nearly 200GB of unified memory.

Why choose a Pro GPU (RTX PRO) over a Consumer GPU (GeForce)?

While the RTX 5090 is an incredible value for raw compute, Pro GPUs offer three critical advantages for enterprise:

Memory Density: The RTX PRO 6000 Blackwell features 96GB of VRAM per card (vs. 32GB on the 5090), allowing you to fit significantly larger models on a single device.
Scalability: Pro cards use blower-style coolers and lower power profiles, allowing us to stack 4x cards in a single workstation. The RTX 5090’s massive size and heat output generally limit it to 1-2 cards per system.
Stability: Drivers and hardware are validated for 24/7 compute workloads, ensuring your week-long training run doesn’t crash on day 6.

Infrastructure & Deployment

Do I need a rackmount server, or can I use a tower workstation?

Develop (Tower Workstations): Perfect for office environments. Our towers are acoustically engineered to run up to dual RTX 5090s or quad RTX PRO cards quietly enough to sit beside your desk.
Scale (Rackmount Servers): Essential for dedicated server rooms. These systems prioritize maximum airflow and compute density (supporting 4-8 GPUs) and are designed for 24/7 uptime in a datacenter.

Do you support Linux for AI development?

Yes! We offer full support for Ubuntu and other major Linux distributions. Our Labs team verifies these configurations with the latest NVIDIA drivers, CUDA, and container toolkits, ensuring you can skip the setup headache and start coding immediately.

Strategy & ROI

Why build vs. rent (AWS/Azure)?

Cloud is great for “bursting,” but owning hardware wins on:

Cost: Renting the equivalent of a 4x GPU node in the cloud can exceed the purchase price of a Puget workstation in just 4-6 months of heavy use.
Data Sovereignty: Keep sensitive proprietary data (finance, healthcare, IP) on local hardware, physically disconnected from public cloud providers.
Iteration Speed: Developing locally removes upload/download latency, allowing for faster experimentation cycles—especially when working with terabyte-scale datasets.