The NVIDIA DGX Spark and DGX Station are both designed as personal AI supercomputers, but they cater to different needs and offer distinct capabilities. Here are the main differences between them:
Size and Form Factor
- DGX Spark: It is the world's smallest AI supercomputer, designed to fit into a compact form factor. This makes it highly portable and suitable for researchers, students, and developers who need powerful AI capabilities in a small space[1][2].- DGX Station: It is a more traditional desktop workstation, offering data center-level performance in a larger form factor. This design allows for more powerful components and better cooling, making it suitable for demanding AI workloads[1][4].
Processing Power and Memory
- DGX Spark: It features the NVIDIA GB10 Grace Blackwell Superchip, which includes a Blackwell GPU with fifth-generation Tensor Cores and FP4 support. This delivers up to 1,000 trillion operations per second (TOPS) of AI compute. The system comes with 128GB of unified LPDDR5x memory[1][2].- DGX Station: It is powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, featuring a Blackwell Ultra GPU with the latest Tensor Cores and FP4 precision. It boasts a massive 784GB of coherent memory space, significantly more than the DGX Spark, making it ideal for large-scale AI training and inference workloads[1][5].
Networking and Connectivity
- DGX Spark: It supports ConnectX-7 networking, allowing users to connect multiple Sparks for collaborative work on large AI models[2].- DGX Station: It features the NVIDIA ConnectX-8 SuperNIC, which supports networking speeds up to 800Gb/s. This enables fast connectivity between multiple DGX Stations for large-scale AI workloads and efficient data transfers[1][5].
Power Consumption and Operating System
- DGX Spark: It operates at a relatively low power consumption of 170W, making it energy-efficient. The system runs on NVIDIA's custom DGX OS, a version of Ubuntu Linux[2][4].- DGX Station: While specific power consumption details for the new DGX Station models are not provided, previous versions of the DGX Station were designed to be water-cooled and operate quietly, suggesting a focus on performance over power efficiency. It typically runs on Ubuntu Desktop Linux OS[3][6].
Availability and Target Audience
- DGX Spark: Reservations for DGX Spark are open, targeting researchers, students, and developers who need compact AI computing solutions[1][7].- DGX Station: It is expected to be available later this year from major manufacturers like ASUS, BOXX, Dell, HP, Lambda, and Supermicro. It is aimed at professionals and organizations requiring high-performance AI capabilities on the desktop[1][7].
In summary, the DGX Spark is ideal for those needing portable, high-performance AI computing, while the DGX Station is designed for more demanding AI workloads requiring extensive memory and networking capabilities.
Citations:
[1] https://nvidianews.nvidia.com/news/nvidia-announces-dgx-spark-and-dgx-station-personal-ai-computers
[2] https://www.pcmag.com/news/what-is-nvidias-dgx-station-a-new-specialized-desktop-line-for-ai-work
[3] https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/dgx-station/dgx-station-print-explorer-datasheet-letter-final-web.pdf
[4] https://www.maginative.com/article/nvidia-unveils-dgx-spark-and-dgx-station-desktop-ai-supercomputers-for-the-developer-masses/
[5] https://www.techpowerup.com/334300/nvidia-announces-dgx-spark-and-dgx-station-personal-ai-computers
[6] https://en.wikipedia.org/wiki/Nvidia_DGX
[7] https://www.constellationr.com/blog-news/insights/nvidia-launches-dgx-spark-dgx-station-personal-ai-supercomputers
[8] https://www.e4company.com/wp-content/uploads/NVIDIA_DGX-STATION_DATASHEET_E4_w.pdf
[9] https://www.reddit.com/r/hardware/comments/1jej1uk/nvidia_announces_dgx_spark_and_dgx_station/