Custom AI Hardware & Workstations

Own Your AI Infrastructure. No Cloud. No Limits.

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Why Custom Hardware Matters

Cloud AI is convenient — until you add up the bills. Every API call, every inference, every image generation, every token processed costs money. For organizations running AI workloads daily, those per-request fees compound fast. A busy team can burn through thousands of dollars per month in API costs alone.

Beyond cost, there are deeper problems with relying entirely on cloud AI:

  • Privacy concerns: Your data leaves your network. You are trusting a third party with sensitive information, trade secrets, or client data. Even with privacy guarantees, you don’t control what happens to your inputs.
  • Latency: Every request travels to a remote data center and back. For real-time applications, that delay is unacceptable. Local hardware responds in milliseconds, not seconds.
  • Internet dependency: If your internet goes down, your AI goes down. For critical workflows, that’s a single point of failure you don’t control.
  • Vendor lock-in: You’re at the mercy of pricing changes, model deprecations, terms of service updates, and service outages. When someone else owns the infrastructure, they make the rules.
  • Data sovereignty: Regulatory requirements may prohibit certain data from leaving your premises or your country. Cloud providers may not satisfy those constraints.

Custom AI hardware eliminates every one of these problems. You own the machine. You control the data. You set the rules. No per-query fees, no privacy concerns, no latency, no internet required. Your AI runs on your terms, in your facility, under your control.

⚙️ What We Build

We design and build custom AI hardware tailored to your specific workloads — from single-GPU workstations for individual researchers to multi-GPU servers for teams, to fully air-gapped AI production studios that never touch the internet. Hardware ships assembled, tested, and documented. Custom AI software configuration is available as a separate engagement — because getting the software right is where the real engineering happens.

What We Build

Three tiers of custom AI hardware, each designed for different needs and scales. Every build is tailored — these are starting points, not rigid packages.

💻 Custom AI Workstations

Single-GPU desktop systems built for individual researchers, developers, and content creators. Powerful enough to run local LLMs, Stable Diffusion image generation, and AI-assisted development locally.

  • 1 high-VRAM GPU (16GB–48GB options)
  • High-core-count CPU (8–24 cores)
  • 64GB–256GB system RAM
  • Fast NVMe storage (2TB–8TB)
  • Quiet, office-friendly operation
  • Runs 7B–70B parameter models locally

🏭️ Multi-GPU Servers

Rack-mountable servers with multiple GPUs for teams, research labs, and production AI workloads. Designed for concurrent users, larger models, and sustained throughput.

  • 2–8 GPUs with high-speed interconnects
  • Dual-socket CPU options available
  • 256GB–1TB+ system RAM
  • Enterprise-grade NVMe storage arrays
  • Redundant power supplies
  • Remote management (IPMI/BMC)
  • Runs 70B+ parameter models, multi-user access

🔒 Air-Gapped AI Production Studios

Fully self-contained AI systems with zero internet dependency. Complete privacy, complete sovereignty. For organizations that cannot — or will not — connect their AI to any external network.

  • No network connectivity required
  • All models and software pre-loaded
  • Physical media for updates and model transfers
  • Complete data isolation guarantee
  • Optional TEMPEST-rated enclosures
  • Ideal for classified, proprietary, or regulated data

Platform Options: Intel vs AMD

We build on both Intel and AMD platforms. Each has strengths depending on your workload, budget, and scalability requirements. We help you choose the right foundation — we have no brand loyalty, only performance loyalty.

Ⓜ Intel Platforms

Mature ecosystem with broad compatibility and proven reliability for AI workloads.

  • Xeon W & Core i9: High single-thread performance, excellent for development workloads
  • Thunderbolt 4: High-speed peripheral connectivity for external storage and expansion
  • Quick Sync Video: Hardware-accelerated video encoding/decoding for content creation pipelines
  • ECC memory support: Error-correcting RAM on Xeon platforms for data integrity
  • Broad driver support: Maximum compatibility with AI frameworks and tools
  • Best for: Workstations, content creation, development environments, single-GPU systems

Ⓝ AMD Platforms

Exceptional multi-core value and PCIe lane abundance for multi-GPU configurations.

  • Threadripper PRO: Up to 128 PCIe Gen5 lanes — critical for multi-GPU servers
  • EPYC server CPUs: Massive core counts (up to 96 cores) for parallel processing
  • More PCIe lanes: Each GPU gets full bandwidth without lane splitting — better multi-GPU performance
  • ECC memory standard: Error correction available across more platform tiers
  • Cost efficiency: Generally more cores and lanes per dollar at the high end
  • Best for: Multi-GPU servers, research clusters, high-throughput production systems

💡 How We Decide

The platform choice depends on your GPU count, workload type, and budget. Single-GPU workstations often favor Intel for clock speed and ecosystem maturity. Multi-GPU servers almost always favor AMD Threadripper PRO or EPYC for PCIe lane allocation. We lay out the trade-offs in plain language and recommend based on your actual needs — not what we have in stock.

GPU Configurations

The GPU is the heart of any AI system. VRAM capacity determines what models you can run; GPU count determines how many users you can serve simultaneously. We configure based on your models, your team size, and your budget.

Configuration VRAM (Total) Models Supported Concurrent Users Best For
1x 24GB GPU 24GB 7B–34B models (quantized 70B) 1–2 Individual developers, small models
1x 48GB GPU 48GB 7B–70B models at full precision 1–3 Researchers, content creators
2x 24GB GPU 48GB 70B models, parallel inference 3–5 Small teams, development
4x 24GB GPU 96GB 70B+ models, fine-tuning workloads 5–10 Research labs, small production
4x 48GB GPU 192GB Large models, multi-model serving 10–20 Production AI, enterprise teams
8x 48GB GPU 384GB Largest open models, training/fine-tuning 20+ users Large organizations, research clusters

VRAM numbers are illustrative — actual configurations are tailored to your specific model requirements and budget. We support NVIDIA RTX, NVIDIA L40S/SXM, and select AMD ROCm GPUs depending on your software stack preferences.

Air-Gapped & Offline Capability

Some organizations need more than privacy — they need absolute isolation. An air-gapped AI system has no connection to any external network. No internet. No Wi-Fi. No Bluetooth. No outbound anything. Data goes in via physical media only, and nothing leaves without your explicit action.

This is the gold standard for data sovereignty. Government agencies, defense contractors, healthcare organizations handling PHI, law firms managing privileged client data, and research institutions with proprietary data all benefit from true air-gapped AI.

🚪 Zero Network Exposure

No Ethernet, no Wi-Fi, no Bluetooth. The system physically cannot connect to any network. Network interfaces can be disabled at the hardware level or removed entirely. Nothing gets in or out electronically.

💾 Physical Data Transfer

Models, datasets, and updates are transferred via physical media — USB drives, external SSDs, or optical discs. We establish secure transfer protocols so your air gap is maintained without compromising workflow.

🔒 Complete Privacy

No telemetry, no phone-home, no usage analytics. Your AI usage stays on the machine. No one knows what you’re running, what you’re asking, or what you’re generating. Complete deniability and confidentiality.

🛡️ Tamper-Evident Options

For high-security environments, we offer tamper-evident chassis seals, physical intrusion detection, and secure enclosure options. You’ll know if anyone has accessed the hardware without authorization.

What’s Included

Every system we build is delivered with hardware, assembly, configuration, testing, and documentation. Custom AI software installation and pipeline deployment are available as a separate engagement — quoted based on your specific software requirements.

💻

Custom-Built Hardware

Hand-selected components chosen for your specific AI workloads. Professional assembly with cable management, thermal optimization, and burn-in testing. No off-the-shelf configurations — every part is specified for your needs.

📦

AI Software Stack — Optional Add-On

Custom software configuration is the most time-consuming and critical part of any AI hardware build. It is quoted separately from hardware and includes:

  • Ollama — Local LLM serving with OpenAI-compatible API
  • ComfyUI — Advanced Stable Diffusion workflow engine for image generation
  • llama.cpp — High-performance CPU/GPU inference engine
  • Open WebUI — Browser-based chat interface (like ChatGPT, but local)
  • vLLM — High-throughput serving for production workloads
  • Text-generation-webui (Oobabooga) — Flexible model testing and chat
  • Stable Diffusion WebUI (Automatic1111) — Image generation interface
  • Whisper — Local speech-to-text transcription
  • Additional tools based on your specific use cases

Software installation, model configuration, pipeline deployment, and optimization are billed separately. This is where the real engineering happens — hardware is the foundation, software is what makes it actually work for your specific needs.

🧮

Pre-Loaded AI Models

We pre-load the models you need so you’re productive on day one. This may include Llama, Mistral, Qwen, DeepSeek, Phi, and other open-weight models in your preferred sizes. Image generation models (Stable Diffusion variants) are pre-downloaded and configured. No waiting for multi-gigabyte downloads — everything is ready to run.

🛠️

Configuration & Optimization

Every component is tuned for your workloads. GPU memory allocation, CPU affinity, storage I/O scheduling, power management, and cooling profiles are all configured based on what you’ll actually be running. We benchmark your system with your real workloads, not synthetic tests.

✅

Testing & Validation

Every system undergoes 24–72 hours of stress testing before delivery. GPU stability under sustained load, thermal performance, memory integrity, storage benchmarking, and real-world inference benchmarks. You receive a test report documenting performance and stability.

📝

Documentation & Training

Complete documentation covering your hardware specifications, software stack, model inventory, maintenance procedures, and upgrade paths. We provide a walkthrough session so your team knows how to use everything. Optional ongoing support packages available.

📦

Warranty & Support

Manufacturer warranties on all components (typically 3–5 years). We handle warranty claims on your behalf if issues arise. Optional extended support packages include remote troubleshooting, software updates, and model upgrades delivered to your system.

Decentralized Infrastructure: You Own Everything

The cloud AI model is fundamentally about dependency. You depend on the provider’s uptime, pricing, model availability, terms of service, and goodwill. When OpenAI changes their API, you adapt or break. When a provider raises prices, you pay or leave. When a model is deprecated, you migrate. You’re renting your intelligence from a landlord who can change the lease at any time.

Custom hardware inverts that relationship. You own the compute. You own the models. You own the data. You own the infrastructure. No one can shut you off, raise your rates, change your terms, deprecate your models, or peek at your data. Your AI capability is a capital asset, not a recurring liability.

This is what we mean by decentralized AI infrastructure: the compute lives where you need it, under your control, answering to no one but you. It’s the difference between renting and owning — and for organizations that use AI daily, ownership pays for itself.

📊 The Break-Even Math

A team spending $500/month on AI API calls is spending $6,000/year. A custom workstation that handles those same workloads locally might cost $8,000–$15,000 upfront including hardware and software configuration. In 16–30 months, the hardware pays for itself. After that, your marginal cost of AI inference is electricity. For higher API spend, the break-even point comes even faster — and you own the asset permanently.

Who This Is For

🔬 Researchers

Academic and industry researchers who need dedicated compute for experiments, model fine-tuning, and large-scale inference without competing for shared cluster resources or paying per-query cloud fees.

🔒 Businesses with Sensitive Data

Organizations handling proprietary data, client information, trade secrets, or regulated data that cannot leave their network. Legal firms, healthcare providers, financial services, defense contractors.

💻 AI Developers

Developers building AI applications who need a local environment for rapid iteration, testing, and development. No API rate limits, no per-call costs during development, full control over the inference stack.

🎬 Content Creators

Artists, designers, video producers, and media creators using AI for image generation, video processing, audio transcription, and content production. Unlimited generation without per-image or per-minute costs.

🏭️ Small & Medium Businesses

Companies that have outgrown API-based AI due to cost or volume, and want to bring AI workloads in-house. Predictable capital expense instead of unpredictable monthly API bills.

🏢 Privacy-First Individuals

Power users, privacy advocates, and independent professionals who want AI capabilities without sending their data to tech companies. Your questions, your prompts, your data — all stay local.

Pricing

Every system is custom-quoted based on your requirements — GPU count, platform choice, RAM, storage, and configuration complexity. Below are rough ranges to set expectations. Pricing includes hardware, professional assembly, thermal optimization, burn-in testing, and delivery documentation.

GPU choice is the single biggest cost driver. Consumer GPUs (RTX 5090, 5060 Ti) keep builds in the lower range. Data center GPUs (NVIDIA A100, H100) push builds into the upper range and beyond. We help you choose the right GPU for your workload and budget.

Entry Workstation

$2,000–$3,500

Single GPU (16–24GB VRAM), 64–128GB RAM, 2TB NVMe. Runs 7B–34B models locally. For individual users and developers.

Pro Workstation

$8,000–$25,000+

Single high-VRAM GPU (48GB) or dual GPUs, Threadripper platform, 128–256GB ECC RAM, 4TB+ NVMe. Runs 70B models at full precision. For researchers and power users.

Multi-GPU Server

$15,000–$50,000+

2–4 GPUs, Threadripper PRO or dual-socket, 256GB–1TB ECC RAM, enterprise storage. For teams and production workloads. GPU choice (consumer vs data center) drives final cost.

AI Production Studio

$40,000–$150,000+

4–8 GPUs, air-gapped configuration, full production stack, tamper-evident options. Consumer GPUs keep builds near the low end; data center GPUs (A100/H100) push builds well past $100K. For organizations needing maximum capability and isolation.

💳 How Quotes Work

We start with a conversation about what you want to run, how many people will use it, and what constraints matter to you (privacy, budget, space, noise). From there we spec a system and provide two itemized quotes: one for hardware (components, assembly, testing) and one for software (installation, configuration, model setup, pipeline deployment). You approve each separately. No hidden margins — you see exactly what each part costs.

Build Timeline

From quote approval to delivery, most builds take 2–4 weeks. Complex configurations or hard-to-source components may extend timelines — we’ll communicate any delays immediately.

1

Consultation & Requirements (Days 1–3)

We discuss your workloads, model requirements, user count, privacy needs, and budget. You receive a detailed spec sheet and itemized quote for approval.

2

Component Sourcing (Days 3–10)

We source all components from trusted suppliers. Most parts arrive within a week. Specialized or high-demand components (e.g., certain GPUs) may take longer — we notify you immediately if availability affects timeline.

3

Assembly & Software Installation (Days 10–16)

Professional assembly with thermal optimization and cable management. Operating system installation, AI software stack deployment, model downloads, and configuration tuning.

4

Testing & Burn-In (Days 16–21)

24–72 hours of stress testing. GPU stability, thermal validation, memory integrity, storage benchmarks, and real-world inference testing with your target models. Performance report generated.

5

Delivery & Walkthrough (Days 21–28)

System delivery or pickup. We walk you through the hardware, software stack, model usage, maintenance procedures, and documentation. You’re productive on day one.

Why Choose Us

Building AI hardware isn’t just about slapping parts together. It requires understanding thermal dynamics, power delivery, PCIe topology, memory bandwidth, and how all of those interact with specific AI workloads. A system that benchmarks well but thermally throttles under sustained inference is a failure. A beautifully assembled machine with a misconfigured software stack is an expensive paperweight.

We’ve been building hardware and systems for 38 years. That’s not a marketing number — it’s decades of hands-on experience with computer architecture, from the days when “building a computer” meant soldering components onto boards to today’s multi-GPU AI servers. We understand hardware at a level that comes only from doing it for a very long time.

More importantly, we use what we build. Our own AI infrastructure runs on the same kind of systems we build for clients. We run local LLMs daily. We generate images locally. We develop and test AI applications on our own hardware. When we recommend a configuration, it’s because we’ve run that configuration ourselves and know how it performs under real workloads — not because we read a spec sheet.

We’re not a reseller pushing boxes. We’re builders who understand the full stack — from silicon to software — and we stand behind every system we deliver.

Common Questions

Do I need to be technical to use one of your systems?

No. Every system ships with a browser-based interface (Open WebUI) that works like ChatGPT — you type a question, you get an answer. The complexity is handled under the hood. We provide a walkthrough so you understand the basics, and documentation for anything you want to explore further. If you can use a web browser, you can use our systems.

Can I upgrade the hardware later?

Yes. We design systems with upgrade paths in mind. Most workstations support additional RAM, storage, and GPU upgrades. Servers are built with expansion slots and power headroom for adding GPUs over time. We document upgrade options for your specific system and can perform upgrades for you if preferred.

What happens when new AI models come out?

You download them and run them. That’s the advantage of owning your hardware — new open-weight models are available the day they’re released, and you simply download and load them. For air-gapped systems, we provide model update services via physical media. Optional support packages include periodic model refreshes delivered to your system.

How much power do these systems use?

It depends on configuration. A single-GPU workstation typically draws 400–600W under load — comparable to a gaming PC. Multi-GPU servers can draw 1,500–3,500W and may require dedicated circuits. We provide power requirements for your specific configuration and can advise on electrical setup if needed.

Can I run ChatGPT/GPT-4 locally on these systems?

You cannot run proprietary models like GPT-4 locally — those are only available via OpenAI’s API. However, you can run open-weight models like Llama 3, Mistral, Qwen, and DeepSeek that approach or match GPT-4-class performance on many tasks. The open-weight ecosystem is advancing rapidly, and local models are increasingly competitive with proprietary offerings.

What about cooling and noise?

Workstations are built for office environments with quiet cooling solutions — large slow-spinning fans, acoustic damping, and efficient thermal design. They’re not silent, but they’re not disruptive. Multi-GPU servers are louder and typically housed in server rooms, closets, or dedicated spaces. We discuss your environment during consultation and design accordingly.

Do you offer support after delivery?

Yes. All components carry manufacturer warranties (typically 3–5 years) which we help you exercise if needed. We offer optional extended support packages that include remote troubleshooting, software updates, model upgrades, and periodic health checks. We’re also available for ad-hoc support — if something isn’t working, call us.

Can you build a system for a specific framework I use?

Almost certainly. We work with the full open-source AI ecosystem — PyTorch, TensorFlow, JAX, Hugging Face libraries, LangChain, LlamaIndex, and many others. If you have a specific framework, model, or pipeline requirement, tell us during consultation and we’ll ensure the system is configured and tested for your exact stack.

What’s the difference between your systems and a pre-built AI PC from a big manufacturer?

Pre-built systems are configured for general purposes and profit margins, not your specific workloads. They come with bloatware, generic configurations, and limited software setup. Our systems are purpose-built for your AI workloads, tuned for your specific models, loaded with a complete open-source AI stack, stress-tested under real conditions, and backed by someone who understands the full stack and is there to support you. The difference becomes obvious the first time you use one.

Do you ship outside the Phoenix area?

We’re based in Avondale, AZ and primarily serve the Phoenix metro area with in-person delivery and setup. For clients elsewhere, we ship systems with remote setup support and video walkthroughs. For air-gapped or high-security systems, we prefer in-person delivery — contact us to discuss your location and needs.

Ready to Own Your AI Infrastructure?

Stop renting your intelligence. Tell us what you want to run, how many people need access, and what matters to you — privacy, budget, performance, or all of the above. We’ll design a system that fits and provide an itemized quote with no obligation.

Contact Steve About Custom Hardware