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NVIDIA’s Grace Blackwell desktop AI lineup consists of two very different systems: DGX Spark, a compact GB10-based developer computer with 128GB of unified memory, and DGX Station, a much larger GB300-based enterprise workstation with hundreds of gigabytes of coherent memory. Both bring parts of NVIDIA’s data-center AI platform closer to users, but neither is a universal replacement for a conventional PC, a multi-GPU server, or cloud computing.
DGX Spark is listed at $4,699 in the U.S. NVIDIA Marketplace as of August 16, 2026. DGX Station is ordered through NVIDIA partners, with no standard public price listed on NVIDIA’s current product page.
What NVIDIA actually unveiled
NVIDIA first introduced these systems at CES on January 6, 2025. The compact system was initially called Project DIGITS; NVIDIA later renamed it DGX Spark. The company also introduced DGX Station, a substantially larger deskside system for enterprise AI teams and research organizations.
The announcement was followed by several separate milestones:
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- Warranty Disclosure: The original manufacturer’s warranty is void due to hardware upgrade. This product is covered by a 1-Year seller warranty and LIFETIME seller tech support from the date of purchase.
- LOCAL LLM DEVELOPMENT AND INFERENCE: Built for AI developers and machine learning engineers who want to prototype, test and run generative AI locally. The GB10 Grace Blackwell Superchip and 128GB unified memory are designed to support inference with models up to 200 billion parameters and fine-tuning with models up to 70 billion parameters.
- AI AGENTS, RAG AND CODING WORKFLOWS: Create private chatbots, coding assistants, autonomous agents, tool-using applications and retrieval-augmented generation systems. Local processing reduces dependence on cloud APIs and gives developers greater control over models, data, latency and ongoing usage costs.
- PRIVATE ON-PREMISES AI FOR TEAMS: Designed for startups, enterprises and professional creators that need to keep proprietary code, models and sensitive datasets within their own environment. Its compact desktop form factor, 10Gb Ethernet and ConnectX-7 networking make it practical for offices, laboratories and multi-system AI development.
- ROBOTICS, COMPUTER VISION AND EDGE AI: Suitable for developers creating robotics, smart-camera, computer-vision, industrial automation and edge AI applications. Prototype perception pipelines, multimodal models and intelligent systems locally before moving validated workloads to compatible production infrastructure.
- January 6, 2025: NVIDIA announced Project DIGITS and DGX Station.
- May 19, 2025: NVIDIA announced partner-built DGX personal computers.
- October 13, 2025: NVIDIA announced that DGX Spark systems were shipping to developers.
- February 2026: NVIDIA raised the U.S. DGX Spark Founders Edition MSRP from $3,999 to $4,699.
- May 31/June 1, 2026: NVIDIA announced a Windows version of DGX Station planned for Q4 2026.
The original announcement is covered in NVIDIA’s January 2025 release, while later product and shipping updates came through the company’s DGX announcement, partner launch and shipping announcement.
What “Grace Blackwell” means
Grace Blackwell is not a normal desktop computer containing a replaceable CPU and a conventional discrete GeForce card. Grace refers to NVIDIA’s Arm-based CPU architecture, while Blackwell refers to the company’s GPU and AI-acceleration architecture.
In these systems, CPU and GPU resources are integrated into a tightly coupled package using NVIDIA’s NVLink-C2C interconnect and a coherent memory architecture. That design can make it easier to work with models that exceed the dedicated VRAM capacity of a typical consumer GPU. It does not, however, guarantee the same throughput as a discrete accelerator with high-bandwidth dedicated memory.
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DGX Spark is the smaller and more accessible system. NVIDIA positions it for individual developers, researchers, students and small teams that need a local AI development platform rather than a general-purpose desktop.
| Component | DGX Spark |
|---|---|
| Superchip | NVIDIA GB10 Grace Blackwell |
| CPU | 20-core Arm processor: 10 Cortex-X925 and 10 Cortex-A725 cores |
| GPU | Blackwell architecture with fifth-generation Tensor Cores and fourth-generation RT Cores |
| Advertised AI performance | Up to 1 FP4 petaflop, using sparsity |
| Unified memory | 128GB LPDDR5x |
| Memory interface and bandwidth | 256-bit; up to 273GB/s |
| Storage | Current NVIDIA configuration lists 4TB of self-encrypting NVMe M.2 storage; NVIDIA documentation also references 1TB and 4TB configurations |
| Networking | 10GbE, ConnectX-7 up to 200Gb/s, and Wi-Fi 7 |
| Display and USB | One HDMI 2.1a connector and four USB-C ports |
| Operating system | NVIDIA DGX OS |
| Power | 240W power supply; GB10 TDP listed at 140W |
| Dimensions and weight | 150 × 150 × 50.5mm; approximately 1.2kg |
See the DGX Spark product page and hardware guide for the detailed specifications.
How to interpret the 1-petaflop claim
The “up to 1 PFLOP” figure is an advertised FP4 AI-performance figure and uses sparsity. It is not a universal measurement of application throughput and should not be compared directly with dense FP16, FP8 or benchmark results.
For local AI, the 128GB memory pool is often more important than the headline compute number. Memory capacity determines whether a model and its runtime data can fit; memory bandwidth, kernels, context length and batch size strongly affect how quickly it runs.
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DGX Station is based on NVIDIA’s GB300 Grace Blackwell Ultra Desktop Superchip. It is designed for substantially larger models, professional development and shared use by an enterprise team or research lab.
- NVIDIA advertises up to 20 petaflops of AI performance.
- The current DGX Station product page lists 748GB of coherent memory.
- Earlier NVIDIA announcement material cited 784GB. These figures should not be silently merged; the current product page and earlier launch material differ.
- NVIDIA says the system can support models of approximately 1 trillion parameters, depending on quantization, architecture, context length, runtime overhead and workload.
- The system can be configured with up to one additional RTX PRO Blackwell-generation GPU.
- Earlier launch material described ConnectX-8 networking up to 800Gb/s and support for partitioning the system into as many as seven MIG instances.
DGX Station is therefore closer to a local AI server or shared workstation than to a compact personal computer. Its model-capacity claims do not mean that every trillion-parameter model will run quickly or comfortably. Loading a model is different from achieving useful latency, throughput or training performance.
NVIDIA’s current DGX Station page provides the current product positioning and ordering path.
DGX Spark versus DGX Station
| Question | DGX Spark | DGX Station |
|---|---|---|
| Main chip | GB10 Grace Blackwell | GB300 Grace Blackwell Ultra |
| Memory | 128GB unified memory | 748GB on the current page; earlier launch material cited 784GB |
| Advertised AI performance | Up to 1 FP4 PFLOP | Up to 20 AI PFLOPS |
| Physical role | Compact desktop developer system | Large deskside enterprise workstation |
| Model guidance | Up to 200B parameters on one Spark; up to 405B in a dual-Spark setup, according to NVIDIA documentation | NVIDIA targets models up to approximately 1T parameters |
| Best fit | Local prototyping, inference, RAG, agents and fine-tuning | Large-model development, local enterprise inference and shared team compute |
| Main limitation | 128GB shared memory, modest bandwidth and limited upgradeability | Cost, power, cooling, size and enterprise procurement complexity |
| Buying path | NVIDIA Marketplace and channel partners | Order through an NVIDIA partner |
What can DGX Spark run locally?
DGX Spark is best understood as a local AI experimentation and inference platform. Realistic uses include:
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- Extreme AI Performance: Powered by NVIDIA GB10 Grace Blackwell Superchip delivering 1 petaFLOP of AI performance and 128GB memory for 200B model fine-tuning.
- Developer-Optimized Platform: Designed for AI developers building secure, long-running agentic workflows, with compatibility across frameworks such as OpenClaw and NemoClaw, supporting private on-device inference, sandboxed execution, and governed data access.
- Scalable Architecture: Featuring NVIDIA NVLink-C2C for ultra-fast CPU-GPU memory communication and NVIDIA ConnectX-7 networking to support dual GX10 system stacking, unlocking superior scalability and performance.
- Advanced Thermal Design: Engineered cooling ensures sustained high performance and reliability in an ultra-small form factor.
- Full Stack AI Solution: The GB10 and NVIDIA AI software stack provide a full stack solution for AI development and deployment.
- Running and evaluating open-weight language models.
- Building retrieval-augmented-generation systems without sending every prompt or document to a cloud service.
- Developing autonomous agents and testing tool-use workflows.
- Performing parameter-efficient fine-tuning and other model adaptation tasks within the available memory and software support.
- Testing a model locally before moving it to a data center or cloud deployment.
- Connecting two Spark systems for larger model capacity or distributed workloads.
NVIDIA’s documentation cites support for models up to 200 billion parameters on one Spark and up to 405 billion parameters with two systems. Those are capacity and platform guidance claims, not promises of high-throughput training or interactive response times.
A model’s parameter count is only the starting point. Weights, temporary buffers, the KV cache, context length, batch size and framework overhead all consume memory. A model that loads in 128GB may become impractical when the context window or number of simultaneous users increases.
Software and Arm compatibility
These are turnkey AI platforms rather than ordinary mini PCs. DGX Spark ships with NVIDIA DGX OS and is designed around NVIDIA’s CUDA software ecosystem, model tooling and networking stack. NVIDIA’s 2026 software updates emphasize agent workflows, newer open models, NemoClaw and inference improvements.
However, DGX Spark uses an Arm-based CPU. Developers should verify:
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- Whether Python wheels and native dependencies are available for the target architecture.
- Whether third-party libraries, drivers and build tools support the installed CUDA and DGX OS versions.
- Whether an existing x86 Linux workflow depends on binaries that cannot be rebuilt.
CUDA support does not automatically make every x86 application compatible with Arm64. Officially supported containers and frameworks are safer than assuming that a package will work because it runs on another NVIDIA GPU system. NVIDIA’s DGX OS documentation is the appropriate reference for the supported software environment.
The announced DGX Station for Windows is a separate product configuration planned for Q4 2026. It should not be treated as evidence that DGX Spark runs Windows as its primary supported operating system.
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DGX Spark
As of August 16, 2026, the U.S. NVIDIA Marketplace lists the DGX Spark Founders Edition at $4,699. NVIDIA previously announced a $3,999 MSRP, but that is no longer the current listed U.S. price. NVIDIA attributed the February 2026 increase to memory supply constraints; the company’s notice is available on its developer forum.
The Marketplace listing includes a 128GB unified-memory system with 4TB storage and advertises a 90-day NVIDIA AI Enterprise license. That should not be interpreted as lifetime inclusion. Pricing, configurations, taxes, availability and support can vary by country and sales channel.
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NVIDIA also announced DGX Spark systems from authorized partners. Partner models may differ in chassis, storage, support and pricing.
DGX Station
NVIDIA’s current DGX Station page directs prospective buyers to contact a partner rather than publishing a standard retail price. That makes the system an enterprise procurement decision involving configuration, support, power, cooling and deployment requirements.
The Windows version of DGX Station was announced for Q4 2026. An announced target window is not the same as general availability, so buyers should confirm availability directly with NVIDIA or an authorized partner.
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- Extreme AI Performance: Powered by NVIDIA GB10 Grace Blackwell Superchip delivering 1 petaFLOP of AI performance and 128GB memory for 200B model fine-tuning.
- Developer-Optimized Platform: Designed for AI developers building secure, long-running agentic workflows, with compatibility across frameworks such as OpenClaw and NemoClaw, supporting private on-device inference, sandboxed execution, and governed data access.
- Scalable Architecture: Featuring NVIDIA NVLink-C2C for ultra-fast CPU-GPU memory communication and NVIDIA ConnectX-7 networking to support dual GX10 system stacking, unlocking superior scalability and performance.
- Advanced Thermal Design: Engineered cooling ensures sustained high performance and reliability in an ultra-small form factor.
- Full Stack AI Solution: The GB10 and NVIDIA AI software stack provide a full stack solution for AI development and deployment.
Who should buy DGX Spark?
DGX Spark makes the most sense for an individual developer, researcher or small team that values a turnkey local CUDA environment and needs more model memory than a typical consumer GPU provides.
It is particularly compelling when:
- Local inference or prototyping is frequent enough to justify dedicated hardware.
- Data should remain on premises, while recognizing that local hardware still requires security, updates, backups and access controls.
- The user wants to experiment with larger open models without managing a multi-GPU server.
- Compact size and relatively low power matter.
- The buyer accepts limited upgradeability and Arm64 compatibility work.
It is a poor fit for buyers seeking a gaming computer, a general-purpose workstation, maximum graphics performance, conventional component upgrades or the highest raw throughput per dollar.
Who should buy DGX Station?
DGX Station is aimed at enterprise AI teams, research labs and professional users who can justify a high-capacity shared system. Its value is likely to depend on utilization, support and the ability to keep large models local, not merely on consumer-style price-performance comparisons.
Buyers should plan for substantially greater power, cooling, noise, space and procurement requirements than DGX Spark. DGX Station is still a single workstation, not a replacement for a multi-rack training cluster.
When cloud or another workstation is better
Cloud GPUs remain preferable for burst workloads, large-scale distributed training or teams that need several accelerators only occasionally. They avoid hardware ownership and maintenance, although recurring usage, data transfer and data-residency requirements can change the economics.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesA self-built multi-GPU workstation can offer greater upgradeability or stronger throughput for a technically capable buyer, but it does not provide the same integrated GB10 or GB300 memory architecture and turnkey DGX environment.
A high-end RTX workstation may be better for mixed workloads, graphics, gaming and familiar x86 software. It may also provide stronger performance for some GPU-optimized tasks, while offering less unified memory for very large models.
Multiple DGX Spark systems can increase total model capacity, but distributed execution adds networking, synchronization and orchestration complexity. A dual-Spark setup is not simply equivalent to one larger accelerator.
The practical verdict
NVIDIA is bringing data-center-oriented AI hardware and software closer to the desk, but “AI supercomputer” is marketing shorthand rather than a guarantee of universal workstation performance. DGX Spark is a compact, specialized platform for local model development and inference. DGX Station is a far larger enterprise system for workloads that need much more coherent memory and shared capacity.
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The right choice depends first on whether the target model, context and workload fit in memory, then on throughput, software compatibility, utilization, power and total cost. For many users, Spark can be a useful local AI appliance. For large teams, Station may provide a powerful shared node. For irregular workloads or large-scale training, cloud infrastructure or a conventional multi-GPU system may remain the more practical choice.
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