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Compact desktop AI system with matte black finish and subtle green accent lighting

NVIDIA

NVIDIA 940-54242-0000-000 DGX Spark AI edge system 20-core Arm 128 GB LPDDR5x 4 TB NVME

SKU CH-HZJF-QX8JW MPN 940-54242-0000-000

A compact AI workstation with a 20-core Arm processor, 128 GB LPDDR5x unified memory, 4 TB NVME storage, and Wi-Fi 7 connectivity

  • Processor20-core Arm,10 Cortex-X925 + 10 Cortex A725
  • Memory size128 GB LPDDR5x, unified system memory
  • Storage driveUp to 4 TB NVME.M2 with self-encryption
  • GPUNVIDIA Blackwell Architecture
  • CPU20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm
  • Wi-Fi standardWi-Fi 7

Type NVIDIA DGX Spark

  • Up to 1 petaFLOP of AI compute at FP4 precision with Blackwell architecture
  • 128 GB LPDDR5x unified memory runs models up to 200 billion parameters locally
  • 20-core Arm CPU (10 Cortex-X925 + 10 Cortex-A725) delivers 64-bit high-performance processing
  • 4 TB NVME self-encrypting storage with 10 Gbps LAN and Wi-Fi 7 connectivity
  • Pre-installed NVIDIA DGX OS and AI stack enables immediate development, fine-tuning and inference

DGX Spark personal AI computer

The NVIDIA DGX Spark is a desktop AI workstation built on the GB10 Grace Blackwell Superchip. It combines a 20-core Arm processor with 10 Cortex-X925 and 10 Cortex-A725 cores alongside a Blackwell GPU. The system ships with 128 GB of unified LPDDR5x memory and a 4 TB NVME SSD. It runs NVIDIA DGX OS for local AI development, fine-tuning, and inference.

240 W power supply and thermal profile

The unit operates from a 240 W power supply in a compact form factor. Unified memory reduces data movement between CPU and GPU, lowering overall power draw during model workloads. Wi-Fi 7 and Bluetooth 5.4 connectivity add minimal thermal load. The 10 Gbps LAN port supports high-throughput transfers without active cooling noise in typical office environments.

Preinstalled NVIDIA AI software stack

DGX OS includes the full NVIDIA AI software stack pre-optimised for the GB10 Superchip. This enables immediate prototyping with models up to 200 billion parameters using FP4 precision. ConnectX networking allows linking two systems to scale to 405 billion parameter models. The self-encrypting NVME drive protects model weights and datasets at rest.

How it compares

Measured against everything we stock in Industrial PCs & AI Edge Systems, at today’s prices. Recalculated every night.

Price $4,053.99
Pricier than about 93% of the 406 comparable items (median $518.49).
RAM capacity 128 GB LPDDR5x
Higher than about 99% of the 39 comparable items (median 8 GB).
SSD storage 4 TB NVMe
Higher than about 99% of the 26 comparable items (median 256 GB).

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Specifications

Product
Brand nameNVIDIA
Model nameDGX Spark Founders Edition
Part number940-54242-0000-000
Product typeGaming & Entertainment
Recommended useConsumer
Processor
Processor20-core Arm,10 Cortex-X925 + 10 Cortex A725
CPU20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm
Memory
Memory size128 GB LPDDR5x, unified system memory
RAM capacity128 GB LPDDR5x
Storage
Storage driveUp to 4 TB NVME.M2 with self-encryption
SSD storage4 TB NVMe
Optical driveNo
Graphics
GPUNVIDIA Blackwell Architecture
Connectivity
Wi-Fi standardWi-Fi 7
Network speed10 Gbit/s
Wi-Fi802.11be Wireless LAN
Bluetooth support5.4
Ports & slots
Video connectors1 x HDMI 2.1a
Rear USB ports4 x USB Type-C
Ethernet port1 port
Power
Power supply240 W
Size & build
Overall dimensions1.99 in × 5.91 in × 5.91 in
Product weight2.65 lb (1.2 kg)
Shipping weight3.18 kg
Package size245 × 245 × 152 mm
Software
Operating systemNVIDIA DGX OS

Questions about this item

What operating system ships on the system and is it ready for AI development?

NVIDIA DGX OS comes pre-installed and is pre-optimised for AI development, fine-tuning, and inference workloads.

How many AI model parameters can this system handle locally?

The 128 GB unified memory supports models up to 200 billion parameters for local prototyping and inference.

Can two units be linked together for larger models?

Yes, NVIDIA ConnectX networking allows two systems to work together on models up to 405 billion parameters.

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