5 Things You Need to Know About NVIDIA DGX Spark

A Desktop Form Factor Built for Enterprise-Scale AI

As AI models continue to scale beyond 100 billion parameters, local development is becoming harder to support using standard workstations or cloud-based virtual machines. Developers, researchers, and ML engineers need a way to run large models, test performance, and iterate quickly without immediately committing to rack-scale infrastructure.

NVIDIA DGX Spark (formerly NVIDIA Project DIGITS) was created for this stage of development. It brings the core technologies used in DGX BasePOD and SuperPOD into a compact form factor, with 1,000 AI TOPS of compute and 128 GB of unified memory. It runs on the same Grace Blackwell architecture used in NVIDIA’s most advanced systems such as the NVIDIA DGX GB200 and supports the full NVIDIA AI software stack, giving developers access to the same tools, workflows, and containerized environments used in production.

This post outlines five lesser known advantages of DGX Spark that make it more than just a compact GPU system. Each feature supports long term scalability, reduces early development friction, and provides technical value beyond what most workstations can offer.

1. Spark runs on Grace, an Arm-based CPU used in next-gen DGX systems

A Practical Platform for Prototyping on Arm

DGX Spark introduces a shift in how developers interact with system architecture. Instead of relying on x86 processors, Spark is powered by the Grace CPU, a 20 core Arm processor directly linked to the Blackwell GPU using NVLink C2C. Both the CPU and GPU share access to a unified 128 GB memory pool, improving efficiency for AI workflows that involve preprocessing, inference, and frequent data exchange between compute elements.

This design is foundational to Grace Blackwell systems at larger scale. DGX Spark gives developers an accessible way to start building and testing workloads that take advantage of these features. For example, memory-intensive tasks like prompt formatting, token handling, or multimodal data pipelines can run entirely within this shared memory space without the overhead of moving data across interfaces.

Early Access to Production Infrastructure Standards

With systems like the DGX B200 and the upcoming DGX GB300 BasePOD and SuperPOD moving to Grace based architectures, DGX Spark acts as a low barrier testbed for compatibility and performance tuning. It allows developers to validate AI workloads on Arm before making larger infrastructure decisions. This is especially useful for teams preparing models and pipelines for eventual deployment in high density data centers, where power efficiency and memory architecture will impact scaling strategy.

Developers can also use Spark to assess software behavior on Arm platforms under real load conditions. From library compatibility to performance tuning, DGX Spark gives technical teams a way to prepare for where NVIDIA infrastructure is headed without committing to rack-level hardware up front.

Compatible with Common Development Tools

Although the system architecture is different, DGX Spark supports a wide range of standard tools. Major frameworks like PyTorch and TensorFlow are fully supported, and many containerized workflows from NGC run on Arm with little to no modification. Data science tools such as RAPIDS and Dask also run natively, enabling teams to test data processing, feature engineering, and evaluation pipelines within familiar environments.

Some low-level dependencies or custom builds may still require updates for full compatibility, particularly if your code relies on architecture-specific libraries or performance tuning. DGX Spark gives developers the ability to identify and address these gaps early, using the same architecture that will power NVIDIA’s next generation infrastructure.

2. You can run 200B parameter models locally with full data control

Inference Support for Models up to 200 Billion Parameters

DGX Spark gives developers the ability to run inference on some of the largest publicly available AI models using local hardware. With 128 GB of unified memory and support for FP4 with sparsity, the system can load and execute models up to 200 billion parameters. This includes models such as LLaMA 2, DeepSeek-V2, and Mistral, along with in-house foundation models built for private applications.

Running inference locally allows teams to evaluate performance, measure latency, and test prompt behavior without needing to allocate external GPU instances. It also creates space for deeper experimentation, such as comparing quantization strategies or observing output variation across different fine-tuned weights.

Secure Development with Data Kept On-Prem

For many organizations, sending data to the cloud introduces risk or violates internal policy. DGX Spark makes it possible to keep sensitive datasets in a local, secure environment while still testing and tuning high-capacity models. Developers can work with production data during model validation, log results directly to local systems, and audit outputs without moving assets into third party platforms.

This approach is especially relevant for teams in industries with compliance requirements or internal review processes such as healthcare, government, finance, or defense. DGX Spark supports a full development cycle where all data stays within your control.

Built for the Transition to On-Prem AI Infrastructure

For teams preparing to deploy larger scale systems, DGX Spark acts as an initial build platform that matches the behavior of NVIDIA's data center systems. Applications and containers developed on Spark will be compatible with DGX BasePOD, SuperPOD, or other Grace Blackwell based infrastructure.

AMAX supports this process with deployment planning, integration services, and validation support to help customers move from initial development into full on-prem deployment when ready.

3. The full NVIDIA AI software stack runs natively on DGX Spark

Preloaded with Enterprise Grade AI Tools

DGX Spark ships with the same software stack used across the DGX platform, including DGX BasePOD and SuperPOD. This includes NVIDIA DGX OS, a custom Linux-based operating system built for AI workloads, along with container runtimes, GPU drivers, and libraries that support deep learning and data science development. The system comes ready to use, allowing developers to begin running containers and launching jobs without additional configuration.

Included tools support inference, training, model packaging, and MLOps workflows. This provides continuity across the development lifecycle, from early model testing to deployment-ready containers. The software environment is maintained by NVIDIA and updated regularly, which helps reduce friction during setup and long-term development.

Optimized Containers and Pretrained Models from NGC

DGX Spark provides access to the NVIDIA NGC catalog, where developers can pull optimized containers for frameworks such as PyTorch, TensorFlow, and JAX. It also includes pretrained models and sample pipelines that are performance tuned for NVIDIA hardware. These resources help teams move faster by offering validated starting points for LLMs, vision models, recommender systems, and other common workloads.

Because these assets are aligned with production scale infrastructure, developers can be confident that models built or tested on Spark will scale cleanly when moved to multi-node systems. This also reduces the effort needed to refactor applications or rework configuration files when transitioning into production.

Support for Microservices and Workflow Tools

DGX Spark supports modern AI development workflows, including microservice deployment and model serving through NVIDIA Inference Microservices. Developers can containerize models using tools like Triton, deploy REST or gRPC endpoints for local inference, and use the same pipeline definitions they would use on a DGX BasePOD.

Tools like NVIDIA AI Workbench and Blueprints are also supported, helping standardize experimentation and improve reproducibility. For teams that build internal tools or pipelines using container orchestration, DGX Spark offers a consistent and well-documented platform to develop and test those workloads in advance of larger deployments.

4. Two Spark units can be clustered to support 405B parameter models

Compact Scaling for Demanding Model Sizes

DGX Spark can be directly linked to a second unit using the built-in ConnectX 7 SmartNIC, allowing developers to scale model size and memory capacity beyond what a single system can handle. In this two-node configuration, Spark supports running models with up to 405 billion parameters using FP4 with sparsity. This is valuable for teams working with advanced LLMs or mixture of experts models, where model size can impact output quality, reasoning depth, and token-level control.

Running inference at this scale allows developers to test how performance, accuracy, and latency behave across large parameter counts without relying on external infrastructure. It also provides a more realistic environment to assess model architecture decisions, token window behavior, and memory tradeoffs. For teams planning to move into larger distributed deployments, clustering two Spark systems offers a practical starting point for evaluating scale-related performance characteristics within a local development workflow.

5. Spark is part of the DGX platform and scales into larger deployments

Designed for Compatibility Across DGX Platforms

DGX Spark is part of the same platform architecture that powers DGX Station, DGX BasePOD, and DGX SuperPOD. It runs the Grace Blackwell GB10 Superchip and uses the same memory model, interconnect, and system software as its larger counterparts. This makes Spark a development-grade system that fits naturally into the broader DGX infrastructure. For teams building containerized workloads, training pipelines, or inference services, Spark provides an entry point that does not require adapting or reworking code for future deployment.

Aligned with Enterprise Tools and Cloud Workflows

Because Spark runs the full DGX software stack, it supports workloads built with NVIDIA AI Enterprise, as well as environments connected to DGX Cloud. This includes tools for orchestration, model serving, and monitoring, along with support for secure multi-user development. Teams can validate their workflows locally on Spark, then deploy those same containers in a multi-node on-prem system or a hybrid configuration that includes DGX Cloud resources. The behavior remains consistent across platforms, which reduces development time and helps teams maintain reliable performance benchmarks.

Scalable with Support from AMAX

For organizations planning to move from local prototyping to production-grade AI infrastructure, AMAX provides the services needed to scale with confidence. This includes guidance on rack-scale DGX BasePOD deployments, DGX Cloud integration, and custom liquid-cooled SuperPOD builds. AMAX helps ensure that the work started on DGX Spark remains directly transferable to the systems that will carry it into production. Whether the next step is adding compute capacity or migrating workloads into a larger deployment, AMAX supports the full lifecycle of DGX-based AI infrastructure.

Built to Scale with You

DGX Spark offers a practical way to bring large-model development in house without the overhead of full rack-scale infrastructure. With support for Arm-based architectures, 200B+ parameter inference, and the full NVIDIA AI software stack, it provides a clear path from prototyping to production.

Whether you're starting with a single unit or planning for larger DGX deployments, AMAX can help design and support your infrastructure every step of the way.