Build an AI server quote without needing the exact spec
Use the interactive builder to create a clear quote brief for GPU workstations, rack AI servers, local inference, rendering, data processing and high-memory workloads.
Create a quote-ready server brief in under a minute
Pick the closest options and the builder will generate a clean specification brief you can copy or send with the quote form. This keeps the page helpful without forcing you to read a long technical article first.
1What do you want to run?
Select the main workload. You can mention mixed requirements in the notes.
2Preferred build type
Choose the format closest to what you have in mind.
3GPU preference
VRAM is usually the key buying decision for AI workloads.
4Memory, storage and deployment
These options help us avoid under-specifying the platform.
5Extra notes
Optional: paste a model name, application, budget range, preferred brand or anything else useful.
Send your AI server quote request
Your generated specification will be added into the form automatically. Add your contact details and any final notes before submitting.
Choose the closest build type, then we’ll refine the specification
These are not fixed bundles. They are practical starting points for quote-led AI server and GPU workstation discussions.
Single-GPU AI workstation/server
For smaller local LLMs, AI agents, development, rendering and GPU-assisted workflows.
- 1x GPU
- 64GB–128GB RAM
- 1–2TB NVMe
Dual-GPU AI server
For heavier inference, multi-model work, rendering queues and teams needing more GPU capacity.
- 2x GPUs
- 128GB–256GB RAM
- 2–4TB NVMe
4U GPU rack server
For dense GPU compute, lab environments, datacentre deployments and higher power/cooling requirements.
- 2–4+ GPU options
- 256GB–512GB+ RAM
- Rack power/cooling checks
High-memory AI/data server
For analytics, large context workloads, virtualisation and data-heavy processing where RAM matters.
- 256GB–1TB+ DDR5 ECC
- RDIMM/LRDIMM options
- NVMe + bulk storage
What to decide before requesting a GPU server quote
AI and GPU server builds are quote-led because the right choice depends on workload, GPU power, memory capacity, storage speed, networking and physical server constraints.
VRAM matters most for AI
For local inference and AI agents, GPU memory capacity often matters more than raw gaming performance. Larger models generally need more VRAM.
Rack vs workstation
Workstations are easier for offices and labs; rack servers are better for datacentres, higher GPU counts, serviceability and controlled cooling.
Power and cooling
Multi-GPU builds need power, airflow and chassis planning. This is why the quote route is better than a one-click checkout for server builds.
| Area | What to think about |
|---|---|
| GPU | What is the workload: local AI, inference, rendering, model testing, CUDA compute or general GPU acceleration? |
| Memory | High-memory workloads may need 256GB, 512GB or 1TB+ DDR5 ECC memory, depending on dataset and workload size. |
| Storage | NVMe is usually preferred for fast datasets, scratch space and high-throughput processing. |
| Networking | 10Gb or 25Gb networking may be needed if large datasets move between workstations, storage and servers. |
| Server platform | HPE, Dell or custom builds may be suitable depending on compatibility, availability, support and budget. |
| Licensing | Consider whether the build needs Windows Server, Linux, remote management, support or additional application licensing. |
Built around the products your workload needs
This page connects the higher-value pieces of the VeriLicense catalogue: memory, GPU hardware, server builds, networking and licensing.
DDR5 ECC server memory
High-memory workloads need the right RAM type, speed and capacity. Check RDIMM, LRDIMM and HPE SmartMemory options.
View DDR5 memoryNVIDIA GPU options
Source individual RTX cards or discuss quote-led GPU requirements for graphics, AI and GPU-accelerated workloads.
View NVIDIA productsHPE and Dell server builds
Spec chassis, CPU, RAM, storage, RAID, PSU, remote management and networking around the workload.
AI server build questions
Do I need to know the exact GPU before asking?
No. You can tell us the workload, expected users and target budget, and we’ll help narrow down suitable GPU and server options.
Can you quote HPE and Dell servers?
Yes. Send the preferred platform if you have one, or tell us you are open to options. We’ll use the information to guide the quote request.
Should I choose RDIMM or LRDIMM?
It depends on the server platform, CPU generation and memory capacity target. RDIMM is common for many enterprise servers; LRDIMM is used for very high memory density configurations.
Can this be used for local AI workloads?
Yes, subject to the workload and hardware selection. The right configuration depends on model size, GPU memory, dataset size, storage speed and networking needs.
Can I just request pricing for a GPU?
Yes. Use the NVIDIA page or click Request quote and include the GPU model/SKU if you already know what you need.
Is performance guaranteed?
No. We help with sourcing and specification guidance, but performance depends on the final hardware, software stack, configuration and workload.
Need help specifying an AI or GPU server?
Send us the workload and preferred platform. We’ll help you identify suitable server, GPU, memory, storage and networking options.