Nvidia's Mini DGX Spark: Enterprise AI Goes Compact

The DGX Spark looks like a miniature version of Nvidia’s larger DGX systems, complete with the same design cues and gold side panels. Nvidia CEO Jensen Huang even presented the first unit symbolically to Elon Musk—a nod to when he handed over the original DGX-1 to OpenAI in 2016. Compact system with 128 GB of shared memory Inside, the DGX Spark runs on Nvidia’s new GB10 chip, built on the Grace-Blackwell architecture.

It combines 20 Arm cores (10 Cortex-X925 and 10 Cortex-A725) with a Blackwell GPU, fabricated using TSMC’s 3-nanometer process. CPU and GPU are directly connected via NVLink C2C. Memory is the key feature: 128 GB of LPDDR5X with 273 GB/s bandwidth form a shared pool accessible by both CPU and GPU.

Nvidia says this allows local execution of models with up to 200 billion parameters (at 4-bit inference) or roughly 70 billion parameters during fine-tuning. The system includes 6,144 CUDA cores, 192 fifth-generation Tensor Cores, and a theoretical FP4 throughput of 1 petaFLOP. It also comes with a 4 TB NVMe SSD, four USB-C ports, HDMI, 10-Gigabit Ethernet, and two QSFP56 connectors for 200-Gigabit networks with RDMA support.

Multiple DGX Spark units can be linked together through those 200-Gigabit interfaces to form small clusters. Performance: not a speed demon, but dependable According to tests by The Register, the DGX Spark isn’t optimized for raw speed. It can handle larger models than any current consumer GPU, but it runs slower.

When fine-tuning a Llama-3.2 model with 3 billion parameters, the Spark took about 90 seconds per million tokens—roughly twice as long as an RTX 6000 Ada, which quickly hits its 48 GB VRAM limit.