LOCAL AI DEVELOPMENT • NVIDIA GB10 • IN STOCK AT AAAWAVE
Your next AI project needs more than a powerful specification sheet. It needs a system you can put to work. The Acer Veriton GN100 VGN100-UD13 brings NVIDIA GB10, 128GB of unified memory and a 2TB NVMe SSD to a compact desktop—and is currently in stock at AAAWave.
For developers exploring local AI, researchers testing models, or businesses building a proof of concept, it deserves a place on the shortlist. Here is what it offers, how to think about storage, and where ASUS and HP alternatives fit.
- Model: VGN100-UD13 / DT.R6LAA.003
- Compute: NVIDIA GB10 Grace Blackwell Superchip
- Memory: 128GB LPDDR5X unified memory
- Storage: 2TB NVMe M.2 SSD
- Operating system: NVIDIA DGX OS
What can you do with the Acer Veriton GN100?
Acer positions the GN100 for local AI prototyping, inference and fine-tuning, with tools such as PyTorch, Jupyter and Ollama. Its shared memory architecture gives the CPU and GPU access to the same memory pool. That is useful when evaluating models that demand more memory than a typical desktop graphics card provides.
Possible projects include a document assistant, a local chatbot, model evaluation, or a domain-specific fine-tuning experiment. Start with the model and software you intend to use, then check compatibility and memory requirements before choosing your workflow.
Local compute also lets you decide where project data is processed. If privacy is a goal, review the application itself: downloads, external APIs and cloud integrations still affect where information goes.
Test chatbots and document assistants on your own hardware.
Build and evaluate ideas before a wider deployment.
Explore adapting supported models to your project.
Is 2TB enough for your AI workflow?
A 2TB SSD can be a practical starting point for a focused development environment. Capacity planning should include the operating system, containers, model weights, datasets, caches and saved checkpoints. Several versions of the same model can consume more space than expected.
- Choose a focused local setup if you work with a small collection of models and regularly archive old experiments.
- Plan additional storage if you keep extensive datasets, many model variants or frequent checkpoints.
- Keep a backup plan for project files and results, whether you use external storage or a NAS.
SSD capacity and memory serve different purposes. Storage holds your files; memory helps determine which workloads fit while they are running. Moving from 2TB to 4TB does not, by itself, make the AI processor faster.
Acer vs. ASUS vs. HP: comparing the 2TB options
These three configurations use NVIDIA GB10 and 128GB of unified memory. They belong in the same compact local AI category, so buying decisions should also consider the exact configuration, software experience, support terms and delivery timing.
Product photos show chassis design. ASUS and HP images may represent another storage configuration of the same chassis. Refer to the part numbers in the table for the selected 2TB models.
| System | Model / part number | Compute / memory | Storage |
|---|---|---|---|
| Acer Veriton GN100 | VGN100-UD13 / DT.R6LAA.003 | NVIDIA GB10 / 128GB | 2TB NVMe SSD |
| ASUS Ascent GX10 | GX10-GG0020BN | NVIDIA GB10 / 128GB | 2TB NVMe SSD |
| HP ZGX Nano G1n | D10NPUT#ABA | NVIDIA GB10 / 128GB | 2TB SSD option |
Why consider Acer? The GN100 combines the core GB10 platform with a compact chassis and current AAAWave availability. For a project with a near-term start date, having the system available to order is a practical advantage.
Why consider ASUS? The Ascent GX10 offers another compact GB10 implementation with NVIDIA DGX OS. It is worth comparing if your team prefers ASUS hardware or already has an established purchasing relationship with the brand.
Why consider HP? The ZGX Nano family includes HP's ZGX Toolkit for local AI workflows. Teams already using HP may want to evaluate this option alongside their support and procurement requirements. Select D10NPUT#ABA for the 2TB configuration.
This is a specification comparison, not a measured performance ranking. Shared compute specifications do not establish which system runs coolest, quietest or fastest under sustained workloads.
Check your software before you buy
GB10 systems use an Arm CPU and a Linux-based AI environment. Confirm that your required containers, Python packages and tools support the platform. If your project depends on Windows-only or x86-only applications, review those requirements first.
Also distinguish model inference from training. Being able to load a model for inference does not mean the same system can fully train that model. Fine-tuning requirements depend on the method, precision, batch size and context length.
Start your next local AI project with Acer
If you want a compact NVIDIA-based system for local AI development, the Acer Veriton GN100 offers a clear starting point: GB10 compute, 128GB unified memory and 2TB of local storage.
The VGN100-UD13 is currently in stock and ready to ship from AAAWave. Visit the product page for current pricing and availability, or contact sales@aaawave.com for help matching the system to your project.
Shop the Acer Veriton GN100 at AAAWave
Availability statement as of October 1, 2026. Stock and pricing may change. This article covers the 2TB VGN100-UD13 configuration.
