Artificial intelligence development often requires powerful computing hardware, especially when working with large language models, AI agents, and machine learning projects. While cloud platforms provide access to this computing power, some developers and research teams prefer running their models locally.
The NVIDIA DGX Spark brings AI computing to a compact desktop system. Powered by the NVIDIA GB10 Grace Blackwell Superchip, it combines a 20-core Arm processor, Blackwell GPU, and up to 128GB of unified memory. NVIDIA introduced it in October 2025 as the world's smallest AI supercomputer.
In 2026, DGX Spark continues to evolve, with a new 64GB configuration announced and improved options for connecting multiple systems. Here's what it offers, what you can run on it, and what to consider before buying one in Bangladesh.
NVIDIA DGX Spark is a compact AI computer designed for developers, researchers, data scientists, and organizations working with artificial intelligence.
Unlike a conventional desktop PC, it combines NVIDIA's Grace Blackwell architecture with unified system memory and an AI-focused software environment. This allows developers to run, test, and fine-tune supported AI models locally instead of relying entirely on cloud computing.
The system measures just 150 × 150 × 50.5 mm and weighs approximately 1.2 kg. Despite its small size, the 128GB version supports demanding AI development workloads that would be difficult to fit into the memory of many conventional desktop GPUs.
DGX Spark is intended primarily for AI development, not as a traditional gaming PC or general-purpose office computer.
The following specifications apply to NVIDIA's 128GB DGX Spark configuration.
| Feature | Specification |
|---|---|
| Processor | NVIDIA GB10 Grace Blackwell Superchip |
| CPU | 20-core Arm, 10 Cortex-X925 + 10 Cortex-A725 |
| GPU | NVIDIA Blackwell architecture |
| AI Performance | Up to 1 petaFLOP at FP4 precision |
| Memory | 128GB LPDDR5x unified memory |
| Memory Bandwidth | 273 GB/s |
| Storage | 4TB NVMe M.2 SSD in the Ryans configuration |
| Networking | 10GbE Ethernet, ConnectX-7 NIC (200Gbps) |
| Wireless | Wi-Fi 7, Bluetooth 5.4 |
| Ports | 4 × USB Type-C, HDMI 2.1a |
| Operating System | NVIDIA DGX OS |
| Power Supply | 240W |
| Dimensions | 150 × 150 × 50.5 mm |
| Weight | Approximately 1.2 kg |
What does 1 petaFLOP mean? It represents one quadrillion floating-point operations per second. NVIDIA's advertised figure is theoretical peak performance using FP4 precision and sparsity. It should not be interpreted as the performance you will achieve with every AI model or application.
The combination of unified memory, AI processing capability, and NVIDIA's software tools is what makes DGX Spark different from a regular desktop.
One of the most important updates is the introduction of a new memory configuration.
On October 2, 2026, NVIDIA announced a 64GB version of DGX Spark , scheduled to become available through participating manufacturing partners from October 23, 2026.
The new configuration retains the GB10 Grace Blackwell Superchip, DGX OS, and NVIDIA AI software stack.
NVIDIA also introduced Sync Cluster Assistant, designed to simplify connecting two DGX Spark systems through their ConnectX-7 networking interfaces.
This allows developers to start with a single machine and connect another compatible system when they need additional memory and computing resources.
The update makes DGX Spark relevant to a broader range of local AI projects, although the right memory configuration still depends on the size and requirements of the models being used.
Both configurations use NVIDIA's Grace Blackwell architecture, but their memory capacity affects the size of AI workloads they can accommodate.
| Feature | DGX Spark 64GB | DGX Spark 128GB |
|---|---|---|
| Unified Memory | 64GB LPDDR5x | 128GB LPDDR5x |
| Processor | NVIDIA GB10 | NVIDIA GB10 |
| GPU Architecture | NVIDIA Blackwell | NVIDIA Blackwell |
| OS | NVIDIA DGX OS | NVIDIA DGX OS |
| Advertised AI Model Support | Up to 100B parameters | Up to 200B parameters |
| Suitable For | Smaller local AI models, agents, and development workloads | Larger models, memory-intensive inference, and fine-tuning |
| Availability | Scheduled from October 23, 2026, through selected OEM partners | Existing NVIDIA configuration |
Model parameter limits are NVIDIA's published capabilities, not guaranteed results for every model. Actual requirements depend on precision, quantization, context length, runtime, and available memory.
The 64GB version is worth considering when your projects fit comfortably within its memory capacity. It can be suitable for local AI agents, application development, and supported language models that do not need 128GB of memory.
The 128GB version is more appropriate when you need additional memory for larger AI models, demanding inference workloads, or supported fine-tuning tasks.
For buyers in Bangladesh, the 128GB NVIDIA DGX Spark with 4TB SSD is the configuration offered through Ryans. The newly announced 64GB version should not be assumed to be available locally until its listing and availability are confirmed.
DGX Spark is designed to support several AI development activities. Its main value is the ability to work with capable AI models on a local system.
Developers can use DGX Spark to run supported large language models for text generation, coding assistance, question answering, and application testing.
NVIDIA specifies that the 128GB configuration can support inference with models of up to 200 billion parameters under suitable conditions.
Running a model locally also means developers can process certain datasets without sending every request to a third-party cloud service. Data protection still depends on how the system, software, and network are configured.
Fine-tuning means adapting an existing AI model to a particular task or dataset rather than training a new model entirely from the beginning.
NVIDIA advertises support for fine-tuning models of up to 70 billion parameters on the 128GB DGX Spark.
This can be useful for research experiments, specialized language applications, and domain-specific AI development.
However, the supported fine-tuning method, precision, and software configuration matter. DGX Spark should not be treated as a replacement for large training clusters.
AI agents can carry out tasks such as analyzing documents, assisting with coding, using software tools, or completing multistep workflows.
DGX Spark supports local agent development using NVIDIA's AI software ecosystem and compatible open-source tools.
Teams can experiment with agent behavior, evaluate model responses, and test applications before considering broader deployment.
DGX Spark can support development work involving image recognition, video analysis, robotics, and other computer vision applications.
NVIDIA provides frameworks such as Isaac, Metropolis, and Holoscan for different AI development environments.
For robotics teams, DGX Spark can be used for supported model development and testing. However, suitability for direct real-time control or a particular industrial deployment must be evaluated separately.
Researchers and data scientists can use the system for supported machine learning experiments, model evaluation, and computationally demanding analysis.
Its unified memory architecture is particularly useful when applications need substantial memory accessible to both CPU and GPU.
The main difference is its architecture and intended workload.
A conventional GPU workstation typically combines a desktop processor, system RAM, and one or more discrete graphics cards with dedicated GPU memory.
DGX Spark uses a unified memory architecture in which the CPU and integrated Blackwell GPU share the same memory pool.
This can make it easier to accommodate certain large AI models that would exceed the available VRAM of a smaller discrete GPU.
However, more available memory does not automatically mean faster performance .
A workstation with a powerful discrete NVIDIA GPU may provide better results for some smaller models or GPU-intensive tasks. DGX Spark's advantage depends on the workload, model size, software optimization, and memory requirements.
Another consideration is compatibility. DGX Spark runs an Arm-based, Ubuntu-derived NVIDIA DGX OS. Developers should check whether their applications, Python packages, frameworks, and containers support Linux Arm64.
DGX Spark and cloud AI platforms serve different purposes.
| Consideration | NVIDIA DGX Spark | Cloud AI |
|---|---|---|
| Hardware | Purchased and operated locally | Computing resources rented from a provider |
| Initial Cost | Higher upfront investment | Usually lower upfront hardware cost |
| Running Costs | Electricity, maintenance, and infrastructure | Usage-based charges and service fees |
| Data Processing | Can remain on local infrastructure | Depends on cloud service and configuration |
| Scaling | Limited by available hardware and supported clustering | Additional resources can generally be provisioned |
| Best Fit | Ongoing local development and supported workloads | Large, variable, or short-term computing requirements |
DGX Spark can reduce reliance on cloud resources for workloads that fit within its capabilities. But it does not eliminate every cloud requirement.
For occasional experiments, renting cloud GPU resources may be more economical than buying dedicated hardware.
For continuous local development, especially where control over infrastructure and data matters, DGX Spark may be worth evaluating.
The better financial choice depends on actual usage, model requirements, cloud pricing, and total operating costs. There is no guaranteed cost-saving figure that applies to every organization.
The NVIDIA DGX Spark GB10 Grace Blackwell 128GB RAM and 4TB SSD AI Workstation PC is priced at Tk 1,100,000 at Ryans , based on the listing checked on October 15, 2026.
This configuration includes:
NVIDIA GB10 Grace Blackwell Superchip
20-core Arm CPU
128GB LPDDR5x unified memory
4TB NVMe SSD
NVIDIA Blackwell GPU architecture
NVIDIA DGX operating system
The product listing specifies a 3-year warranty , with the original box required for warranty claims. It also states a 45-day delivery period .
You can review the specifications and purchasing information on the NVIDIA DGX Spark AI Workstation product page at Ryans .
Note: Price, availability, warranty terms, and delivery estimates may change. Confirm the final details before placing an order.
DGX Spark makes the most sense for users who have specific AI computing requirements rather than someone simply looking for a powerful desktop.
It is worth considering for:
AI developers building and testing large language model applications
Research teams running supported local AI experiments
Universities working on artificial intelligence and machine learning projects
Startups developing AI agents and specialized applications
Organizations that want greater control over where supported AI workloads and data are processed
It may not be the right investment for:
General office users or gamers
Beginners who only use cloud-based AI chat services
Developers whose workloads run comfortably on an existing GPU workstation
Organizations requiring large-scale training infrastructure beyond DGX Spark's capabilities
Before purchasing, identify the models you plan to use, their actual memory requirements, and whether your development tools are compatible with the system.
NVIDIA markets DGX Spark as the world's smallest AI supercomputer. It combines Grace Blackwell computing, unified memory, and NVIDIA's AI software stack in a compact desktop system. It is designed for specific AI workloads and does not provide the same overall computing capacity as a large data-center supercomputer.
Supported AI models can run locally after the necessary software and model files have been installed. Internet access may still be required for downloads, updates, cloud services, and certain integrations.
NVIDIA advertises support for inference with models up to 200 billion parameters on the 128GB configuration. Actual compatibility and performance depend on model format, precision, quantization, context requirements, and available resources.
DGX Spark ships with NVIDIA DGX OS, an Ubuntu-based Linux operating system. It is not supplied as a Windows PC. Software compatibility should be checked for its Arm64 architecture.
Yes. NVIDIA provides ConnectX networking and software for connecting compatible DGX Spark systems. NVIDIA Sync Cluster Assistant helps configure supported multi-system workloads, although scaling benefits depend on the application.
It can be worthwhile for developers and organizations that regularly work with supported AI models requiring substantial local memory. For smaller workloads or occasional experiments, an existing GPU workstation or cloud platform may be a more practical investment.
NVIDIA DGX Spark brings capable AI development hardware into a compact desktop format. With the GB10 Grace Blackwell Superchip, up to 128GB of unified memory, and NVIDIA's AI software ecosystem, it offers a practical option for certain local AI workloads.
The new 64GB configuration and improved clustering tools announced in 2026 expand the ways developers can use the platform.
For researchers, startups, and businesses in Bangladesh, the decision should come down to actual AI workload requirements, software compatibility, and overall cost.
Explore the NVIDIA DGX Spark 128GB AI Workstation at Ryans to review its specifications and purchase details before deciding whether it fits your project.
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