






NVIDIA DGX Spark 4TB Personal AI Supercomputer
- AI Processor: NVIDIA® GB10 Grace Blackwell Superchip — 1 petaFLOP AI Performance (FP4, Sparse); 5th-Gen Tensor Cores; 4th-Gen RT Cores
- CPU: NVIDIA® Grace CPU — Arm 20-core (10× Cortex-X925 + 10× Cortex-A725)
- Memory: 128 GB LPDDR5x Unified System Memory — 256-bit interface, 273 GB/s bandwidth; run models up to 200B parameters
- Storage: 4 TB NVMe M.2 SSD with self-encryption
- Connectivity: Wi-Fi 7 | Bluetooth® 5.3 | 10 GbE (ConnectX-7 Smart NIC @ 200 Gbps) | 4× USB Type-C
- Display Output: 1× HDMI 2.1a + up to 3× DisplayPort via USB-C
- OS: NVIDIA® DGX™ OS — AI software stack pre-installed
- Form Factor: Compact Mini PC — 150 × 150 × 50.5 mm, ~1.14L, 1.2 kg | Cluster 2 units for 405B-parameter models | 1-Year Warranty
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The NVIDIA DGX Spark (4TB) brings petaFLOP-class AI computing to your desk. Powered by the NVIDIA® GB10 Grace Blackwell Superchip with 128 GB LPDDR5x unified system memory and a 4 TB self-encrypting NVMe M.2 SSD, it delivers 1 petaFLOP AI performance (FP4) in a compact ~1.14-litre form factor. Run large language models up to 200 billion parameters, fine-tune models up to 70 billion parameters, or cluster two DGX Spark units to tackle models up to 405 billion parameters. Built on the NVIDIA® DGX™ Spark platform, the DGX Spark ships with the full NVIDIA DGX software stack — ready for AI development, deep learning, data science, fine-tuning, inference, and computer vision workflows straight out of the box.
| AI Performance | |
| Platform | NVIDIA DGX™ Spark |
| Superchip | NVIDIA® GB10 Grace Blackwell Superchip |
| AI Performance (FP4) | 1 PFLOP (petaFLOP) — Sparse, FP4 |
| Tensor Cores | 5th Generation |
| RT Cores | 4th Generation |
| Supported Precisions | TF32, FP16, BF16, INT8, FP8, FP6, FP4 |
| NVENC / NVDEC | 1× NVENC / 1× NVDEC |
| CPU & Memory | |
| CPU | NVIDIA® Grace CPU — Arm 20-core (10× Cortex-X925 + 10× Cortex-A725) |
| System Memory | 128 GB LPDDR5x Coherent Unified System Memory |
| Memory Interface | 256-bit |
| Memory Bandwidth | 273 GB/s |
| Max Model Size (Inference) | Up to 200 billion parameters |
| Max Model Size (Fine-Tuning) | Up to 70 billion parameters |
| Storage | |
| Primary Storage | 4 TB NVMe M.2 SSD (Self-Encrypting) |
| Connectivity | |
| Wi-Fi | Wi-Fi 7 |
| Bluetooth | Bluetooth® 5.3 |
| Ethernet | 1× RJ-45 10 GbE |
| Smart NIC | NVIDIA ConnectX-7 Smart NIC (200 Gbps) |
| USB | 4× USB Type-C |
| Display Outputs | |
| HDMI | 1× HDMI 2.1a |
| DisplayPort (via USB-C) | Up to 3× DisplayPort 1.4a (DP Alt Mode over USB-C) |
| Audio Output | HDMI Multichannel Audio |
| Max Simultaneous Displays | Up to 4 (1× HDMI + up to 3× via USB-C) |
| Operating System | |
| OS | NVIDIA® DGX™ OS (pre-installed) |
| AI Software Stack | NVIDIA AI software stack pre-installed — optimised tools, libraries, frameworks, and pre-trained model support |
| Dimensions, Weight & Power | |
| Dimensions (L × W × H) | 150 × 150 × 50.5 mm (~1.14L) |
| Weight | 1.2 kg |
| Power Supply | 240 W |
| GB10 TDP | 140 W |
| Multi-Unit AI Clustering | |
| Cluster Interface | ConnectX-7 (QSFP — 200 Gbps) |
| Max Cluster Size | 2 units |
| Combined AI Performance | 2 PFLOP FP4 (2× DGX Spark) |
| Max Clustered Model Size | Up to 405 billion parameters (2× 128 GB unified memory) |
| QSFP Cable | Sold separately |
| AI Superchip | NVIDIA GB10 Grace Blackwell |
|---|---|
| AI Performance | 1 PetaFLOP (FP4) |
| Unified Memory | 128 GB LPDDR5x |
| Model Capacity | Up to 200B params |
| AI Storage | 4 TB NVMe |
| AI Networking | 10 GbE, ConnectX-7 200GbE, Wi-Fi 7 |
| AI CPU | 20-core Arm (Grace) |
| AI Operating System | NVIDIA DGX OS |
| AI Form Factor | Mini |
Frequently Asked Questions
Is the NVIDIA DGX Spark a good choice for local AI development in Bangalore?
The NVIDIA DGX Spark is an excellent choice for local AI development in Bangalore because it packs a one PetaFLOP AI supercomputer into a compact form factor. Local developers prefer this model for running large-scale AI models without relying on cloud latency. It serves as a top-tier solution for serious research and development tasks, allowing teams in the city to maintain high performance and efficiency while keeping their data and processing infrastructure entirely on-site.
What is the current price and availability of the NVIDIA DGX Spark at YourShoppy?
The NVIDIA DGX Spark is available for purchase at YourShoppy for the price listed on our website. We maintain competitive pricing for this high-performance hardware to ensure value for our customers. Because this unit is currently available at our Bangalore store, we strongly recommend that you check our live inventory status and confirm availability with our team before visiting us for a hands-on product demonstration.
Can the NVIDIA GB10 Grace Blackwell chip handle models with 200 billion parameters?
The NVIDIA DGX Spark is fully capable of running models up to 200 billion parameters thanks to its powerful GB10 Grace Blackwell superchip. This system is specifically engineered for heavy workloads and includes 128 gigabytes of LPDDR5x unified memory. This architecture provides the necessary bandwidth and memory capacity to handle complex AI inference tasks efficiently, ensuring that your most demanding models run smoothly without performance bottlenecks.
How does the NVIDIA DGX Spark differ from a standard high-end workstation?
The NVIDIA DGX Spark is a true supercomputer that outperforms standard workstations by utilizing the dedicated NVIDIA DGX OS and the advanced Grace Blackwell superchip. While a typical desktop PC often struggles with massive parameter models, this unit delivers one PetaFLOP of AI performance. It also includes specialized networking capabilities that standard workstations lack, making it a professional-grade tool designed specifically for intensive AI development rather than general computing tasks.
Does the NVIDIA DGX Spark come with pre-installed software for AI development?
The NVIDIA DGX Spark arrives with the NVIDIA DGX OS pre-installed to provide a complete AI software stack right out of the box. This configuration is designed to minimize setup time for our customers. You can start training and deploying your AI models immediately without the hassle of manual driver installations or complex environment management, allowing you to focus entirely on your research and development projects.
What are the connectivity options available on the NVIDIA DGX Spark?
The NVIDIA DGX Spark features a 10 gigabit Ethernet port and a ConnectX-7 Smart NIC capable of 200 gigabits per second for high-speed data transfer. For modern wireless integration, the system also supports Wi-Fi 7 and Bluetooth 5.3. These options ensure that your AI supercomputer maintains a robust connection to your existing network infrastructure, providing maximum throughput and minimal latency for even the most data-intensive AI workflows.
Is the four terabyte storage on the NVIDIA DGX Spark expandable for larger datasets?
The NVIDIA DGX Spark includes a four terabyte NVMe M.2 SSD with self-encryption, which is generally sufficient for high-speed AI model storage. While the internal drive is designed for top performance, many of our customers choose to scale their datasets beyond this capacity by utilizing the high-speed networking options. You can easily connect the system to external storage arrays to accommodate larger data requirements while maintaining fast access speeds.
I am looking for a powerful AI development machine that supports 200 billion parameter inference and includes a high-speed 4TB NVMe SSD.
The NVIDIA DGX Spark (4TB) model 940-54242-0008-000 is designed for your requirements, featuring the NVIDIA GB10 Grace Blackwell Superchip and a 4 TB self-encrypting NVMe M.2 SSD for 200 billion parameter inference.
I need a small-footprint machine with 128GB of unified memory that can handle 200 billion parameter models for my data science projects.
The NVIDIA DGX Spark (4TB) model 940-54242-0008-000 fits in a 1.14-litre form factor and provides 128 GB of LPDDR5x unified system memory to support 200 billion parameter models.
Compare with similar products:
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- CPU: NVIDIA® Grace CPU — Arm 20-core (10× Cortex-X925 + 10× Cortex-A725)
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