AI Startups
Stop renting compute. Own it.
API costs scale with your product. Hardware costs do not. One CPUBLD AI build pays for itself in months — then every inference is free.
AI Infrastructure Division
Custom AI Infrastructure — Built for Private Deployment
Your models. Your hardware. Your building. No API. No rate limits. No data leaving your walls.
Who this is for
Built for teams who need to own their compute
ML Developers
Your model. Your hardware. Your rules.
No rate limits. No cold starts. No vendor dependency. Fine-tune, run, and iterate on hardware you control entirely.
Compliance-Driven Businesses
Your data never leaves your building.
Healthcare, legal, finance — if your data cannot go to an external API, you need local inference hardware. CPUBLD builds it, you run it, your compliance team signs off.
The private AI argument
Why run AI on your own hardware?
Zero data egress
Your prompts, your outputs, your training data. None of it touches an external server.
No rate limits
Run 10,000 queries or 10 million. Your hardware does not throttle you.
Predictable cost
One build cost. Zero monthly API bills. The ROI compounds from day one.
Full control
Run any model. Fine-tune on your own data. Switch models without a vendor agreement.
Build tiers
One ladder, from a developer box to a rack
Every AI build CPUBLD sells sits on this ladder. Starting prices are built from component costs on September 29, 2026; each tier is quoted per configuration at the cost on the day.
AI Workstation
Single GPU. For developers running 7B–14B models locally, or 30B with a 32GB card.
$4,300 starting
Ships within 10–15 business days
- CPU AMD Ryzen 9 9950X — 16C/32T
- GPU RTX 5070 Ti 16GB base · RTX 5080 16GB +$600 · RTX PRO 4500 32GB +$2,300 · RTX 5090 32GB +$6,600
- Memory 64GB DDR5-6000
- Storage 2TB NVMe Gen5 SSD
- Form Tower workstation
Llama 3.x, Mistral, Qwen at Q4. Choose the 32GB card for 30B-class models.
AI Server Node
64GB of VRAM across two cards. Dedicated inference for a team or a product.
$14,500 starting
Timeline quoted at order confirmation
- CPU Threadripper PRO 9955WX — 16C, 128 PCIe lanes
- GPU 2× RTX PRO 4500 32GB base · RTX PRO 5000 48GB · 2× RTX 5090
- Memory 64GB ECC RDIMM · 128GB +$2,600
- Storage 4TB NVMe Gen5 array
- Form Tower or 2U rack-mount
70B models at Q4, team inference, production API workloads.
EPYC Multi-GPU Node
96GB per GPU. Training and full-precision inference on the largest open models.
$32,000 starting
+$18,000 per additional RTX PRO 6000
- CPU AMD EPYC 9355P — 32 cores, 128 PCIe 5.0 lanes
- GPU 1× RTX PRO 6000 Blackwell 96GB base · up to 4× with NVLink
- VRAM 96GB → 384GB
- Memory 128GB ECC RDIMM base · up to 1.5TB
- Storage 4TB Gen5 OS + 8TB NVMe dataset array
- Form 4U rack or tower · IPMI standard
70B–400B+ models without quantization loss, fine-tuning, air-gapped deployments.
Rack Configuration
Multiple nodes built into server racks.
Custom Quote
Consultation only — no fixed price
- Nodes 1U / 2U rack-mount, single or multi-GPU each
- Density Sized to your rack space and power
- Network Interconnect planning included
- OS Ubuntu Server, configured per workload
Companies running production AI at volume across several machines.
Why these prices moved
GPU and memory prices rose sharply through 2026 because AI data centers are buying most of the world's memory supply. As of September 28, 2026 an RTX 5090 is $6,995 at retail against a $1,999 launch price, an RTX PRO 6000 is $16,000 against $8,565, and a 64GB DDR5 kit is $913. CPUBLD does not mark these up beyond the build; quotes are priced at component cost on the day they are sent, and a quote is held for the period stated on it. Read why RTX 5090 prices are so inflated →
Software & setup
Delivered ready to deploy
CPUBLD does not develop the software. We install it, configure it, and validate it on the hardware before it ships — the standard stack below, or the stack your team already uses.
Standard stack — included
Every AI build ships with the stack below installed and validated. Connect it to your network, SSH in, and run your first model. No driver setup, no framework installation.
Your own stack
Already standardized on a stack, an image, or a set of containers? Send it to us. We deploy it on the hardware, validate it, and ship the system ready for your team — nothing to rebuild on arrival.
Setup & deployment — optional
If you want the setup taken off your team entirely, CPUBLD handles it: network configuration, users and access, model loading, and a working first run, on site in the NYC metro or remotely. Ask for it on the request form.
✔
Ubuntu 24.04 LTS
Long-term support through 2029.
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NVIDIA drivers + CUDA Toolkit
Current stable release, validated together on the GPU.
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Ollama
Local model management and inference, configured.
✔
vLLM
High-throughput inference server for API-style workloads.
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Docker
Container runtime for deploying AI services.
✔
SSH + remote access
Configured before shipping. Connect on delivery.
✔
CUDA benchmarks
GPU compute validated before every build ships.
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LM Studio — on request
Desktop interface for non-technical users.
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Windows or a custom image — on request
If your workflow requires it, we build to it.
VRAM sizing guide
How much compute do you actually need?
GPU VRAM is what determines the model size you can run. Match your workload to the right hardware.
| Model size | VRAM needed | Recommended GPU | GPU street price · Sept 28, 2026 |
|---|---|---|---|
| 7B models | 8–12GB | RTX 5060 Ti 16GB | ~$780 |
| 13B–14B models Most Popular | 12–16GB | RTX 5070 Ti 16GB · RTX 5080 16GB | ~$1,120 · ~$1,640 |
| 30B–34B models | 20–32GB | RTX PRO 4500 32GB · RTX 5090 32GB | ~$3,100 · ~$7,000 |
| 70B models | 35–48GB | 2× RTX PRO 4500 · RTX PRO 5000 48GB | ~$6,200 · ~$8,800 |
| 100B+ / Enterprise | 96GB+ | EPYC + RTX PRO 6000 96GB (1–4×) | ~$16,000 per card |
Not sure where you land? Tell us what you are running and we will spec it for you. GPU prices are retail lows tracked by Tom's Hardware on September 28, 2026 and change weekly.
Not sure? Get a spec recommendation →Your data. Your building. Full stop.
CPUBLD AI infrastructure is on-premise by design. The hardware ships to you. Your team installs it on your network. Your models run on it. Your data never leaves your building — not to CPUBLD, not to a cloud provider, not anywhere. For healthcare, legal, and financial services businesses running AI on sensitive data, this is not a feature. It is a requirement.
Build Your Private AI Stack →
Healthcare
Legal
Finance
AI build request
Tell us what you are running
Models, use case, budget, and how you want the software handled. CPUBLD replies within 24 hours with a hardware recommendation and quote.
We review every AI infrastructure request within 24 hours and respond with a custom hardware recommendation and quote.
Request received.
CPUBLD will review your requirements and respond within 24 hours at with a hardware recommendation and quote.
Frequently asked
AI infrastructure questions
API costs scale with usage — as your product or team grows, your monthly AI bill grows with it. On-premise hardware has a one-time cost, and every inference after that is free. Beyond cost, local hardware gives you complete data privacy — your prompts, your training data, and your outputs never leave your building. For teams working with sensitive data or subject to compliance requirements, that is not a preference — it is a necessity.
That depends on GPU VRAM. The AI Workstation with a 16GB card runs 7B to 14B models at Q4 quantization; with a 32GB card (RTX PRO 4500 or RTX 5090) it runs 30B-class models. The AI Server Node with two RTX PRO 4500s (64GB) or an RTX PRO 5000 (48GB) handles 70B models and smaller fine-tuning workloads. The EPYC Multi-GPU Node with up to four RTX PRO 6000 GPUs (384GB via NVLink) runs 70B–400B+ models without quantization loss and supports training. Rack configurations scale beyond that. Not sure where your workload lands? Submit a build request and we will spec it for you.
By default, Ubuntu 24.04 LTS, NVIDIA drivers, CUDA toolkit, Ollama, vLLM, Docker, and SSH, installed and validated before shipping, so the system arrives ready to run. CPUBLD does not develop the software; we install and configure it. If your team already has its own stack, image, or containers, send it and we deploy and validate that instead. If you want the setup handled on your side too, CPUBLD can do the network configuration, access, and first run, on site in the NYC metro or remotely.
Yes. Our Rack Configuration tier builds AI compute nodes into server rack format — 1U and 2U rack-mount options, single or multi-GPU per node, scaled to your density requirements. Rack configurations are custom-quoted based on the number of nodes, GPU configuration, and network interconnect needs. Submit a consultation request and we will design the right rack layout for your infrastructure.
AI infrastructure builds are made to order. Turnaround time depends on the configuration — AI Workstation tier ships within 10–15 business days. AI Server Node and Rack configurations are quoted with a specific timeline at order confirmation based on component availability. Every build undergoes CUDA validation and full inference testing before it ships. You will receive tracking information via email when your order ships.
Ready to build your AI stack?
Tell us what you are running, what you need to scale to, and your budget. We will spec the right hardware for your workload.
Start Your AI Build Request →