GPU Dedicated Servers
for AI, Machine Learning,
and
Rendering.
Full access to dedicated GPU hardware for training, inference, and rendering jobs that run every day, not just once. No shared tenancy, no noisy neighbor slowing down a long training run.
- Radeon 780M servers from
- $382.72/mo
- GPU memory per server
- Shared, 96 GB system RAM
- Location
- Utah
- RTX 4090 servers from
- $798.72/mo
- GPU memory per server
- 24 GB
- Location
- Utah
- RTX A5000 servers from
- $819.52/mo
- GPU memory per server
- 24 GB
- Location
- Los Angeles
- RTX 5090 servers from
- $1,256.32/mo
- GPU memory per server
- 32 GB
- Location
- Utah
- A100 80GB servers from
- $3,128.32/mo
- GPU memory per server
- Up to 320 GB
- Location
- Utah
- A40 servers from
- $4,168.32/mo
- GPU memory per server
- Up to 336 GB
- Location
- Utah
Whole GPUs on bare metal, never shared. 11 GPU servers, real store prices. Compare them all.
Hardware Built for the Workload That Never Turns Off.
A model that trains continuously or serves inference requests around the clock is cheaper and more predictable on dedicated hardware than on pay per use cloud capacity, once the GPU stays busy most of the time.
Where This Actually Gets Used
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Academic & Independent AI Research: Run repeated training and fine-tuning experiments on a fixed budget.
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Machine Learning Teams: Train computer vision or natural language models on private data without shared cloud tenancy.
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Production Inference Startups: Serve live product models where response time and cost both stay predictable.
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Animation & Video Studios: Render 3D scenes and video where GPU throughput directly shortens render times.
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Data Science Teams: Run large-scale simulations and numerical workloads that keep a GPU busy for days.
Ready for your AI stack
Install it yourself with full root, or ask our engineers to set it up as Hire an Expert work.
One NVIDIA A100 running all month
What the monthly bill looks like for a GPU that is busy every hour.
If your app is live, the dedicated price does not move: more users, more requests and more tokens cost you nothing extra. A cloud bill grows with every GPU-hour and every gigabyte sent. Cloud rates: published on-demand A100 prices, October 2026.
Dedicated Wins When the Job Runs Every Day.
Once your model is in use every day, a dedicated GPU server earns its keep: the bill is the same whether it answers ten requests or ten million, the GPU is never shared, and growth makes each request cheaper instead of your bill bigger. Hourly cloud GPUs only make sense for a test that runs for a few hours.
- Whole GPU, Never Shared
- Your Own Drivers and Frameworks
- Your Data, Your Server
- Usable IPv4 Included
- NVMe, SSD or SATA Storage
- Full Root Access
Direct Access to the GPU, Not a Virtualized Slice.
Install the exact driver and framework versions your project needs, with the full GPU available to your workload rather than a virtualized fraction of one.
Delivered in Business Hours
Your server is delivered within 2 to 46 business hours of payment, depending on the location and hardware.
Any Framework You Need
Install the GPU drivers, CUDA or ROCm version and ML frameworks your project needs.
Bare Metal
The GPUs, CPU and memory serve only your workload, with no hypervisor in between.
The range across our 11 GPU servers
11 Configurations, From a Single GPU to Seven GPUs in One Server.
Plans start at $382.72 a month with an AMD Radeon 780M, move through NVIDIA RTX 4090, RTX A5000 and RTX 5090 builds, and go up to servers with two or four NVIDIA A100 or seven A40 GPUs. Billed monthly, no setup fee.
Available in: United States (Utah, Los Angeles).
| Server | CPU and GPU | RAM | Storage | Bandwidth | Price / mo | Order |
|---|---|---|---|---|---|---|
|
Ryzen 7 8700G + AMD Radeon™ 780M
ENTRY LEVEL United States
|
8 cores / 16 threads AMD Radeon™ 780M |
96 GB DDR5 | 1 x 3.84 TB Gen4 NVMe |
50 TB 1 Gbps 5 IPv4 |
$382.72
/mo
|
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Server
Location
Included
|
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|
Ryzen 9 9950X + RTX 4090
BUSINESS United States
|
16 cores / 32 threads RTX 4090 GPU |
96 GB DDR5 | 1 x 3.84 TB NVMe |
50 TB 10 Gbps 5 IPv4 |
$798.72
/mo
|
|
|
Server
Location
Included
|
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|
Ryzen 9 9950X + RTX A5000
BUSINESS United States
|
16 cores / 32 threads RTX A5000 GPU |
96 GB DDR5 | 1 x 3.84 TB NVMe |
50 TB 10 Gbps 5 IPv4 |
$819.52
/mo
|
|
|
Server
Location
Included
|
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|
Dual Gold 6148 + GT 1030
BUSINESS United States
|
40 cores / 80 threads NVIDIA GT 1030 GPU |
512 GB | 4 x 3.84 TB NVMe |
10 TB 10 Gbps 5 IPv4 |
$840.32
/mo
|
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|
Server
Location
Included
|
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|
EPYC 7702 + RTX A5000
BUSINESS United States
|
64 cores / 128 threads RTX A5000 GPU |
128 GB DDR4 | 1 x 6.4 TB NVMe |
50 TB 10 Gbps 5 IPv4 |
$1,048.32
/mo
|
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Server
Location
Included
|
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Ryzen 9 9950X + RTX 5090
HIGH PERFORMANCE United States
|
16 cores / 32 threads RTX 5090 GPU |
96 GB DDR5 | 1 x 3.84 TB Gen4 NVMe |
50 TB 10 Gbps 5 IPv4 |
$1,256.32
/mo
|
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Server
Location
Included
|
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|
EPYC 7443P + 2 x A100 80GB
HIGH PERFORMANCE United States
|
24 cores / 48 threads 2 x A100 80GB GPU |
512 GB | 2 x 3.84 TB NVMe |
100 TB 10 Gbps 5 IPv4 |
$3,128.32
/mo
|
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Server
Location
Included
|
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|
EPYC 7402P + 2 x A100
ENTERPRISE United States
|
24 cores / 48 threads 2 x A100 GPUs |
512 GB | 6.4 TB NVMe |
200 TB 10 Gbps |
$3,618
/mo
|
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Server
Location
Included
|
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|
EPYC 7443P + 4 x A40
ENTERPRISE United States
|
24 cores / 48 threads 4 x A40 GPU |
512 GB | 2 x 3.84 TB NVMe |
100 TB 10 Gbps 5 IPv4 |
$4,168.32
/mo
|
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|
Server
Location
Included
|
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|
EPYC 7443P + 4 x A100 80GB
ENTERPRISE United States
|
24 cores / 48 threads 4 x A100 80GB GPU |
512 GB | 2 x 3.84 TB NVMe |
100 TB 10 Gbps 5 IPv4 |
$6,248.32
/mo
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Server
Location
Included
|
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Dual EPYC 7742 + 7 x A40 NVIDIA
ENTERPRISE United States
|
128 cores / 256 threads 7 x A40 NVIDIA GPU |
512 GB | 15 TB NVMe |
50 TB 10 Gbps 5 IPv4 |
$6,248.32
/mo
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Server
Location
Included
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| No plans match your search. | ||||||
Need a Custom GPU Build, or Paying Too Much for Cloud GPUs?
Tell us what you run. We quote a custom configuration, compare it line by line with your current cloud GPU bill, and help you decide, even if the answer is to stay where you are.
- Custom builds: GPU model and count, memory, disks and location
- A free review of your cloud GPU bill, with a side-by-side monthly cost
- Help choosing the model and the GPU for your workload
- Setup of drivers, CUDA, Docker, vLLM, Ollama or your own stack
Pick the GPU for Where Your AI Is Today.
Most AI projects grow in steps. Start where you are, and move up only when the work needs it. Every step runs on hardware that is yours alone.
Calling AI APIs
Chatbots, agents and n8n workflows that call hosted models. They need a reliable server, not a GPU.
Small private models
Run 7B to 8B models, embeddings for search and Whisper speech-to-text on your own GPU. Fine-tune small models with LoRA.
Mid-size models and images
30B-class models with a shorter context, Stable Diffusion and ComfyUI image work, and faster fine-tuning.
Large private LLMs
70B-class models in 4-bit for a team or a product, with room for long documents.
Training and the largest open models
Fine-tuning and training on your own data, and serving big mixture-of-experts models split across several GPUs.
A build that is not listed
A different GPU, more memory or storage, or a free look at your current cloud GPU bill to see what a dedicated server would save.
Which Model Fits Which GPU?
How much GPU memory a model needs depends on its size and on how it is compressed. These are the usual sizes for models compressed to 4 bits, the common way to serve them.
| Model size | Memory needed (4-bit) | Examples of what it does well | Fits on |
|---|---|---|---|
| 7B to 8B | About 5 to 6 GB | Chat, summaries, classification, data extraction | Any of our GPUs; several copies on one 24 GB card |
| 13B to 14B | About 9 to 10 GB | Better reasoning and writing, coding help | RTX 4090, RTX A5000, RTX 5090 |
| 30B to 32B | About 20 GB, more with long context | Strong general assistant, coding, tool use for agents | RTX 5090 (32 GB), or a 24 GB card with a short context |
| 70B | About 40 to 43 GB | Near top-tier answers for a private assistant or product | One A40 (48 GB) or one A100 (80 GB) |
| 100B and up, mixture of experts | 80 GB and more | The largest open models, research, high-volume serving | Servers with 4 x A100 80 GB or 7 x A40 |
Approximate. Longer prompts and more users at once need extra memory for the context cache; full-precision training needs several times more.
What Changed, and What It Means for Your Server.
Open models and the tools around them have moved fast. Here is what matters when you run AI on your own hardware.
Open models caught up
Open-weight families such as Llama, Qwen, DeepSeek, Gemma, Mistral and OpenAI's gpt-oss now handle most business tasks, and you can run them on your own GPU.
Mixture of experts
Many new models only use part of their weights for each word, so a very large model answers faster, but still needs the memory to hold all of it.
Smaller, sharper compression
4-bit and 8-bit formats such as GGUF, AWQ and FP8 shrink models to a fraction of their size with little loss, so bigger models fit on one GPU.
Faster serving
Servers such as vLLM and SGLang batch many users together and use speculative decoding, so one GPU answers far more requests.
Fine-tuning on one GPU
LoRA and QLoRA teach a model your tone, terms or task by training a small add-on, instead of the whole model.
Agents and MCP
Models now use tools: they search your files, call your APIs and act in your systems through MCP servers you control.
Retrieval (RAG)
Answers come from your own documents, with sources, using embeddings in a vector database next to the model.
Beyond text
Vision models read images and scanned forms, Whisper turns speech into text, and image models generate pictures, all on the same GPU.
Predictable Cost for a Workload That Runs Every Day.
Once a training or inference job runs continuously, a fixed monthly cost on dedicated hardware is easier to plan around than a bill that grows with every hour of cloud usage.
- Whole GPUs, Never Shared
- NVMe Storage on Every Plan
- Full Root / SSH Access
- NVIDIA and AMD Options
- 10 Gbps Ports on Most Plans
- Your Own ML Stack
From Model Training to Serving Real Time Requests.
Whether you are training a custom model on your own dataset, serving inference for a live production application, rendering 3D graphics, or running a daily research pipeline, a dedicated GPU server keeps the same hardware ready for you every day.
View Our PlansA Private ChatGPT for Your Team
An assistant your staff use every day, on an open model that runs on your server, so prompts and answers stay in-house.
Answers From Your Own Documents
Retrieval (RAG) over manuals, contracts, tickets and wikis, with the source of every answer.
Coding Assistants
Code completion and review for your developers on a model that never sends your code outside.
Document and Data Extraction
Invoices, forms, IDs and scanned PDFs read into structured JSON with vision and language models.
Speech to Text
Transcribe calls, meetings and lectures with Whisper, then summarise them with a language model.
Translation and Localisation
Translate products, support replies and documents in many languages, in your own terms.
Fine-Tuning on Your Data
Teach a model your field with LoRA or QLoRA, from customer service tone to legal or medical terms.
Model Testing and Evaluation
Compare models, prompts and settings before launch, and run benchmarks on the same hardware every time.
Medical and Research Training
Train and test models on de-identified data that has to stay inside the institution.
AI Products and APIs
Serve your own model behind an API for your app or your customers, at a fixed monthly cost.
Agents That Do the Work
AI agents that read email, update the CRM and file reports, through MCP servers and your own tools.
Image and Video Generation
Stable Diffusion, ComfyUI and video models for marketing, product shots and design drafts.
Computer Vision
Inspection, counting and safety checks from camera images, trained on your own pictures.
Search and Recommendations
Embeddings that make product and site search understand meaning, and suggest related items.
3D Rendering and Video
Blender, Unreal Engine and video encoding on GPUs that are free whenever you need them.
Augmented and Virtual Reality
Render 3D scenes, build digital twins and stream AR and VR training simulations from a GPU in the data centre.
Scientific Computing
Simulations, data science and CUDA workloads for universities and research teams.
Teams That Need Their Own AI Hardware.
The people renting our GPU servers today, and why they chose dedicated hardware over cloud GPUs.
Simple Monthly Pricing.
Every plan is billed monthly with no setup fee. Your server is delivered within 2 to 46 business hours of payment, depending on the location and hardware. Dedicated servers are not covered by the money-back guarantee, so ask us before you order if you are unsure which plan fits.
- Plans from$382.72/mo
- Setup feeNone
- Delivery2 to 46 business hours
- BillingMonthly
3 Simple Steps to Start.
Delivered within 2 to 46 business hours of payment.
Choose Plan
Select a GPU hardware plan that matches your deep learning model size and inference requirements.
Deploy GPU Server
Complete checkout. Your server is delivered within 2 to 46 business hours, with root login and IPs by email.
Start Managing
Install your drivers and frameworks, and call on our engineers when you need help.
Migration by Our Engineers.
Our engineers can move your training or inference pipeline, models and data to your new server. On an unmanaged server this is on-demand work through Hire an Expert, from $8 per 15 minutes or $28 per hour, billed only for the time used.
Real Feedback From the Businesses We Support
See how our engineering support and infrastructure keep real businesses online.
GPU dedicated server: we build it, hand it over and stay on call
Getting AI and automation into production takes more than hardware. We design and deploy the whole system on your own server, document it and hand it over. After that, you can call us per task or per hour, with no retainer.
Free Basic Managed Support
Every Ucartz server includes Free Basic Managed Support: complimentary, best-effort help with quick tasks of about 5 minutes, such as access checks, restarts, OS reinstalls and DNS pointing.
What is included →Plug-and-play expert help
Model updates, new workflows, scaling or fixes after handover. From $8 per 15 minutes or $28 per hour, with no monthly commitment.
See on-demand support →AI, automation & custom builds
Private LLMs (Ollama, Llama, Mistral), RAG over your documents, n8n and API automations, GPU/CUDA stacks and custom internal tools.
Plan a project →Frequently Asked Questions
Quick answers about the plans, support and billing.
Give Your Model the Same Hardware Every Day
Pick a GPU configuration, or ask us which build fits your training or inference workload.