DGX Spark vs. AI Cube: Which Local AI Hardware Fits Your Business?

Configure the WZ-IT AI Cube as a local AI server - GB10 hardware, complete initial setup, hardening, a local model agreed in advance and support in a defined delivery scope. Schedule an initial consultation

DGX Spark and the WZ-IT AI Cube are not fundamentally different performance classes today. Both use NVIDIA's GB10 class with 128 GB unified memory. Comparing only GPU, memory or advertised FP4 performance therefore means comparing largely the same technical category.

The actual decision is whether you want to buy a vendor appliance and build the production AI stack yourself, or procure a preconfigured system with a defined setup scope.

Table of contents

• The short answer
• Hardware and delivery scope
• What 128 GB unified memory means in practice
• When DGX Spark fits
• When the WZ-IT AI Cube fits
• When both systems are too small
• How to compare costs
• Decision matrix

The short answer

DGX Spark fits when an experienced technical team wants to manage the NVIDIA development environment, select models and build the workspace, identities, data sources, updates and backups internally.

The WZ-IT AI Cube fits when the same compact GB10 class should arrive prepared for plug-and-play use as a local AI server: with a hardened base installation, Open WebUI, Ollama and/or vLLM, a model agreed in advance, documentation and technical integration.

A custom GPU system fits when you need high availability, rackmount, dedicated GPUs, high concurrent throughput or significantly larger memory configurations.

Hardware and delivery scope

The WZ-IT AI Cube is not an alternative to GB10 hardware. It is a defined delivery and setup scope built on that hardware class.

What 128 GB unified memory means in practice

Unified memory is available to both CPU and GPU. This allows quantised models to load that would not fit into common 24 or 48 GB graphics cards. NVIDIA positions one system in this class for models up to about 200 billion parameters and two linked systems for larger models.

That figure only answers the memory question. Productive use also depends on:

• tokens per second for the exact model,
• throughput under concurrent requests,
• system prompt, document context and answer length,
• quantisation,
• stable support for the runtime on Arm and GB10.

A model may fit into memory and still be too slow for ten concurrent users. We therefore size the configuration against the actual workload.

When DGX Spark fits

DGX Spark is a reasonable choice when:

• Linux, containers and local model runtimes are already familiar,
• the NVIDIA software environment is wanted for development or model testing,
• workspace, identity, RAG and operations will deliberately be built internally,
• no defined setup scope from a service provider is required.

For a platform or development team, that internal ownership can be the desired approach.

When the WZ-IT AI Cube fits

The AI Cube is for organisations that do not want to start from an empty hardware platform. The standard configuration includes:

• the compact GB10 appliance with 128 GB unified memory,
• fully configured and hardened Linux with GPU drivers and container runtime,
• Open WebUI for chat, models and knowledge collections,
• Ollama and/or vLLM for local model serving,
• a local model agreed in advance, installed and technically verified,
• initial setup, onboarding and support,
• technical documentation and root access.

Knowledge sources, SSO, custom RAG pipelines, MCP servers, middleware or ongoing managed operations can be added. They are not included under the same fixed scope because requirements and data sources differ by organisation.

When both systems are too small

A single GB10 appliance is not a replacement for every GPU infrastructure design. A custom architecture is appropriate when you need:

• many concurrent users with short guaranteed response times,
• high availability or defined recovery objectives,
• rackmount, redundant power or data centre requirements,
• models or contexts that require more memory or throughput,
• training or substantial fine-tuning rather than mainly inference,
• integration with existing Kubernetes, Proxmox or GPU clusters.

In these cases, we design dedicated GPUs, multi-GPU systems or distributed inference. The AI Cube can remain useful for testing, development or as a separate workload system.

How to compare costs

The market price of a DGX Spark or GX10 appliance varies by supplier, SSD configuration and availability. A useful comparison separates three cost blocks:

1. Hardware - appliance, SSD and accessories.
2. Setup - operating system, runtime, workspace, model, network and identity.
3. Operations - updates, monitoring, backups, model changes and support.

WZ-IT AI Cube Pro Managed costs from EUR 899 excluding VAT per month, plus one-time provisioning and initial setup. The minimum term is six months. This includes hardware, hardening, the preconfigured stack, a local model agreed in advance, documentation and support, monitoring, updates and ongoing technical operations. Custom integrations are scoped separately.

Cloud API costs should only be compared using a real workload profile. Token projections without input/output ratios, context length, caching and utilisation create false precision.

Decision matrix

The hardware class is only the starting point. The important questions are which software should already be configured, who handles integration and who operates the system after handover.

Want to assess the options against your workload? View the AI Cube and delivery scope or schedule an initial consultation.

Related guides

• Local AI inference with the AI Cube
• Ollama vs vLLM
• GPU servers for local AI
• Open WebUI as a self-hosted AI workspace

Sources

• NVIDIA DGX Spark product overview
• NVIDIA DGX Spark hardware documentation
• ASUS Ascent GX10 product overview
• Open WebUI documentation