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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

  • TypeGaming & Entertainment
  • UsageConsumer
  • Processor20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm
  • Processor Main Features64 bit 20-Core Processor
  • Memory128 GB LPDDR5x, unified system memory
  • StorageUp to 4 TB NVME.M2 with self-encryption

Type NVIDIA DGX Spark

  • GMKtec Ryzen Al Max+ 395
  • NVIDIA DGX Spark

取り消し線の付いたバージョンは存在しますが、こちらでは取り扱っていません。

  • 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.

特長

  • 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

仕様

BrandNVIDIA
ModelDGX Spark Founders Edition
Part Number940-54242-0000-000
TypeGaming & Entertainment
UsageConsumer
Processor20 core Arm, 10 Cortex-X925 + 10 Cortex-A725 Arm
Processor Main Features64 bit 20-Core Processor
Memory128 GB LPDDR5x, unified system memory
StorageUp to 4 TB NVME.M2 with self-encryption
GraphicsNVIDIA Blackwell Architecture
Power Supply240W
Operating SystemNVIDIA DGX OS
CPU Type20-core Arm,10 Cortex-X925 + 10 Cortex A725
Memory Capacity128GB LPDDR5x
SSD4TB NVMe
Optical Drive TypeNo
LAN Speed10Gbps
WLAN802.11be Wireless LAN
WiFi GenerationWi-Fi 7
BluetoothBluetooth 5.4
Video Ports1 x HDMI 2.1a
Rear USB4 x USB Type-C
RJ451 port
Dimensions (H x W x D)1.99" x 5.91" x 5.91"
Weight2.65 lbs.
配送重量3.18 kg
梱包サイズ245 × 245 × 152 mm

この商品に関する質問

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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