DGX Spark vs HP ZGX Nano vs Acer Veriton GN100: GB10 Mini Workstation Guide

Direct answer: DGX Spark is the clearest NVIDIA reference system. HP ZGX Nano is the most enterprise-oriented alternative in this group, adding HP’s ZGX Toolkit, regulated-environment options, and a documented two-system scale-out path. Acer Veriton GN100 is a compact GB10 workstation with a strong published UK specification and a conventional Acer channel. All three target local AI, but the best choice depends on software image, support, storage, networking, and region—not merely the fact that each uses GB10.

This is a comparison of published specifications and vendor positioning, not a benchmark. The systems were not physically tested. Availability, price, support, and bundled components should be verified in the buyer’s country before purchase.

The three systems in one table

The table contains vendor-stated categories and listed configurations. “Up to” is not a throughput guarantee, and a regional SKU can differ from the specification page.

DGX Spark: the reference path

NVIDIA describes DGX Spark as a desktop AI system built around the GB10 Grace Blackwell Superchip. The specification includes a 20-core Arm CPU, a Blackwell GPU, 128 GB of coherent LPDDR5x memory, 273 GB/s of bandwidth, 4 TB NVMe M.2 storage, 10 GbE, Wi-Fi 7, and a ConnectX-7 NIC capable of up to 200 Gb/s.

Its main advantage is coherence of the story. NVIDIA documents the hardware, DGX OS, AI software stack, workload categories, and connection to larger NVIDIA infrastructure as one platform. A team that wants a local environment resembling an NVIDIA deployment target may spend less time deciding which image, driver, or container baseline to adopt.

The main caveat is Arm64 validation. Even with CUDA available, Python wheels, native extensions, compilers, monitoring agents, databases, and proprietary tools may need an Arm64 build. Use NVIDIA’s dependency and porting guide before treating an application as portable.

HP ZGX Nano: enterprise workflow around the same class

HP positions ZGX Nano as a small AI workstation for building, fine-tuning, and validating local models. HP lists the GB10 Grace Blackwell Superchip, 128 GB of coherent unified memory, and 2 TB or 4 TB self-encrypted NVMe M.2 storage. It states that workloads can target models up to 200B locally, with two systems connected for up to 405B, subject to the workload and compatible QSFP cable.

HP’s differentiator is the surrounding operating model. The ZGX Toolkit is supplied to help install open-source tools, MLflow, and Ollama, and to pair a client device with the Nano. HP says the client can be Windows 11 or Ubuntu 24.04 (or later) with Visual Studio Code, while the host is the ZGX Nano. This may reduce setup friction for teams whose users work from Windows or macOS but need a shared local AI target.

HP also advertises a regulated-environment variant without Bluetooth or Wi-Fi radios. That matters for buyers with radio restrictions or air-gapped requirements. The page describes remote setup and KVM support through HP Remote System Controller, although remote power-on is not supported. Confirm which of these options are included in the exact quote rather than assuming every ZGX Nano has the same management features.

Acer Veriton GN100: a compact conventional workstation

Acer’s UK specification page lists the Veriton GN100 AI Mini Workstation with a GB10 Superchip, 128 GB LPDDR5X memory, 4 TB PCIe 4.0 NVMe storage, NVIDIA DGX OS, and a maximum 240 W power supply. It lists 150 × 150 × 50.5 mm dimensions and an approximate 1.2 kg weight.

The same page lists Gigabit Ethernet, Wi-Fi 7, Bluetooth 5.4, and an NVIDIA ConnectX-7 NIC described as 200G × 2 QSFP. That combination gives the GN100 a useful connectivity profile for a compact box, but the buyer should verify which ports, cables, and adapters ship in the local package. The UK page is not a global price or availability guarantee.

Acer may be attractive when the organization already buys Acer desktops or when a conventional regional retailer is easier to use than an enterprise sales process. Ask for the exact DGX OS image, driver update path, warranty, and support ownership. “DGX OS” on the specification sheet should still be matched to a supportable release and an acceptance test.

Memory does not equal identical performance

All three systems list 128 GB of unified or coherent memory. That is valuable for local model development because the CPU and GPU can use a shared pool, but it does not mean every model will run at a useful speed. Model weights, quantization, context length, KV cache, batch size, temporary tensors, and runtime overhead all compete for capacity.

The model-size claims should be read as vendor-stated workload categories. A 200B inference statement may assume quantization, a particular runtime, and a limited concurrency. Fine-tuning can require substantially more working memory than inference. Before procurement, run the exact model, context, precision, and serving engine on the exact configuration.

Storage also matters. DGX Spark and Acer list 4 TB as a baseline configuration; HP offers 2 TB or 4 TB. A 2 TB system can be enough for a single controlled workflow, while 4 TB gives more room for checkpoints, container layers, indexes, and datasets. In every case, set a retention policy and backup plan. Local NVMe is not a substitute for recoverability.

Software and maintenance questions

DGX Spark and Acer advertise NVIDIA DGX OS, while HP combines the NVIDIA stack with its ZGX Toolkit. These labels can simplify onboarding, but teams still need to know who maintains the image and which updates are validated.

Ask each vendor for:

• the exact OS and kernel release;
• NVIDIA driver and CUDA support versions;
• container runtime and registry guidance;
• Arm64 package and native-extension support;
• firmware update and rollback procedures;
• support boundaries between hardware, OS, and AI tools.

The team should store a reproducible environment record: image version, firmware, driver, CUDA, container digest, model commit, and benchmark script. This is especially important when an OEM’s update channel differs from NVIDIA’s reference lifecycle.

Networking and physical deployment

These devices are small, but they are often used as networked compute nodes rather than standalone desktops. Plan VLAN placement, firewall rules, remote access, model and dataset transfer, and logging. HP’s two-system scale-out and Acer’s ConnectX listing make QSFP cable and switch compatibility a practical procurement question. NVIDIA’s documented high-speed path is likewise useful only when the surrounding network supports it.

For a shared server, prefer controlled wired access even when Wi-Fi 7 is available. For a personal development unit, wireless convenience may be worth more. Measure wall power and sustained thermals under the actual workload; adapter ratings and TDP are not the same as measured consumption.

Which buyer should choose which?

Choose DGX Spark when:

• NVIDIA’s reference software and documentation are the priority;
• 4 TB storage and a known 128 GB configuration fit the workload;
• local-to-data-center CUDA continuity matters;
• the team can own Arm64 dependency validation;
• high-speed ConnectX networking is part of the design.

Choose HP ZGX Nano when:

• client-device pairing and a managed toolkit reduce onboarding time;
• regulated or radio-restricted deployments are important;
• KVM and fleet-oriented support have business value;
• two-system scale-out is a realistic requirement;
• HP is already an approved enterprise supplier.

Choose Acer Veriton GN100 when:

• a compact Acer workstation fits existing procurement and service processes;
• the listed 4 TB / DGX OS configuration matches the project;
• Wi-Fi 7, Bluetooth, and ConnectX options are useful;
• a regional Acer channel can document warranty and availability;
• the team is comfortable validating its own software baseline.

Choose none yet when:

• the model and context requirements are not measured;
• more than 128 GB is needed in one node;
• the machine will be idle most of the time;
• the supplier cannot provide a current quote and support boundary.

A practical buying test

Use one representative model and prompt set. Measure time to first token, generation throughput, peak memory, model-load time, storage pressure, sustained temperature, restart behavior, and network transfer. Then repeat the test after a clean image restore. For HP, include the ZGX Toolkit workflow; for Acer, include the local DGX OS setup; for DGX Spark, include the documented NVIDIA container path.

The goal is not to crown a universal winner. It is to determine which system can complete the real workload and remain maintainable after the initial demo. A strong support relationship can outweigh a small hardware difference, while a polished toolkit cannot compensate for an incompatible model runtime.

OPINION: DGX Spark is the technical reference, HP ZGX Nano is the strongest managed-enterprise alternative, and Acer GN100 is the straightforward compact OEM option. Choose the system whose software, service, and regional configuration you can actually operate.

This completes the first GB10 comparison cluster. For the larger-memory step beyond this class, see NVIDIA DGX Station and DGX Spark vs cloud GPUs.

Sources and verification note

DGX Spark specifications and positioning were checked against NVIDIA’s DGX Spark page and NVIDIA’s Arm64 dependency guide on August 30, 2026. HP specifications and toolkit details were checked against the HP ZGX Nano AI Station page. Acer specifications were checked against the Acer Veriton GN100 UK page. Prices, availability, bundles, and support terms can change; verify the final regional offer before purchase.