A small AI supercomputer that will revolutionize local AI development.
A compact AI supercomputer "based on NVIDIA DGX Spark" that will revolutionize local AI development.
■ Overview The "MS-C931" is a compact AI supercomputer that condenses GPU-based high-performance computing into a small form factor. Its design, optimized for edge AI and local development environments, enables fast and secure execution of AI model training and inference without relying on the cloud. It is suitable for a wide range of applications, from research and development to prototyping and validation before product implementation. ■ Key Features - Overwhelming processing performance (equipped with GPU) It can be equipped with the latest NVIDIA GPUs, delivering computational performance of several hundred TOPS, capable of handling large-scale deep learning models. - Compact design that fits on a desk It does not require a server room and can be installed on a desk in an office or laboratory. Despite its size being comparable to a typical tower PC, it achieves high-density cooling and power design. - Ideal for AI development in local environments AI development can be conducted independently of the network, making it easy to implement in research institutions and companies with strict security requirements, eliminating cloud communication costs. It can process internal data without exposing it externally, making it particularly effective for AI development involving personal information and intellectual property.
basic information
- Computational Performance: Equipped with NVIDIA GB10 GraceBlackwell Superchip (CUDA 6144 cores + 5th generation Tensor cores), offering up to 1,000 TOPS of FP4 AI performance - CPU: 20-core ARM Grace (Cortex-X925×10 + A725×10) - Memory: 128GB LPDDR5X (coherent sharing between CPU and GPU) - Storage: 1TB or 4TB NVMe M.2 SSD (self-encrypting support) - Size/Weight: Approximately 151×151×52mm, weight about 1.2kg - Connectivity: USB 3.2 Type-C ×4, 10GbE RJ-45, Wi-Fi 7, Bluetooth 5.3 - Power/Cooling: Maximum power consumption approximately 224W, includes 240W PSU, compact chassis cooling design - OS/Framework: Compatible with NVIDIA DGX OS, pre-installed environment for AI development such as Isaac/Holoscan
Price range
Delivery Time
Applications/Examples of results
■ Representative Use Cases - Local learning and fine-tuning of LLMs and image generation AIs - AI model development in highly confidential areas such as healthcare, manufacturing, and finance - Real-time simulations including large-scale AI inference processing - Building on-premises AI infrastructure to avoid cloud dependency - Establishing AI education and experimental environments in educational and research institutions
