The NVIDIA Blackwell GPU in the DGX Spark and the NVIDIA Blackwell Ultra GPU in the DGX Station are both part of Nvidia's Grace Blackwell architecture, designed for high-performance AI computing. However, they cater to different needs and offer distinct performance capabilities.
NVIDIA Blackwell GPU in DGX Spark
The DGX Spark is powered by the GB10 Grace Blackwell Superchip, which includes a Blackwell GPU with fifth-generation Tensor Cores and FP4 support. This setup delivers up to 1,000 trillion operations per second (TOPS) of AI compute performance, making it suitable for fine-tuning and inference tasks with advanced AI models like the NVIDIA Cosmos Reason world foundation model[2][3]. The system features 128 GB of unified system memory, which is LPDDR5X, and a memory bandwidth of 273 GB/s[5][10]. The DGX Spark is designed for developers and researchers who need to prototype, fine-tune, and deploy AI models quickly, especially in edge computing scenarios where data privacy and low latency are crucial[7].
NVIDIA Blackwell Ultra GPU in DGX Station
In contrast, the DGX Station utilizes the GB300 Grace Blackwell Ultra Desktop Superchip, which includes a Blackwell Ultra GPU. This system is designed for more demanding AI workloads, such as large-scale training and inferencing. The DGX Station offers a significant increase in memory capacity with 784 GB of coherent memory space, combining the CPU's LPDDR5x DRAM and the GPU's HBM3e memory[1][8]. The Blackwell Ultra GPU in the DGX Station provides superior performance compared to the standard Blackwell GPU, with capabilities that are more aligned with data center-level performance. It supports the latest Tensor Cores and FP4 precision, connected via NVLink-C2C for enhanced system communication and performance[2][3].
Performance Comparison
- Compute Performance: Both systems deliver up to 1,000 trillion operations per second for AI compute tasks, but the DGX Station's larger memory and more advanced GPU architecture make it better suited for complex AI model training and large-scale inferencing[1][3].
- Memory and Bandwidth: The DGX Station offers significantly more memory (784 GB) compared to the DGX Spark (128 GB), which is crucial for handling large datasets and complex AI models. The memory bandwidth in the DGX Spark is 273 GB/s, but specific bandwidth details for the DGX Station are not provided, though it is expected to be higher due to its more advanced architecture and larger memory capacity[1][5].
- Target Audience: The DGX Spark is aimed at developers and researchers needing immediate AI model deployment and experimentation, while the DGX Station targets professional users and enterprises requiring high computation power for demanding AI tasks[7][10].
In summary, while both GPUs offer high AI compute performance, the Blackwell Ultra GPU in the DGX Station provides superior capabilities for large-scale AI workloads due to its advanced architecture and significantly larger memory capacity.
Citations:
[1] https://www.theregister.com/2025/03/18/gtc_frame_nvidias_budget_blackwell/
[2] https://investor.nvidia.com/news/press-release-details/2025/NVIDIA-Announces-DGX-Spark-and-DGX-Station-Personal-AI-Computers/default.aspx
[3] https://nvidianews.nvidia.com/news/nvidia-announces-dgx-spark-and-dgx-station-personal-ai-computers
[4] https://www.tomshardware.com/pc-components/gpus/nvidia-announces-blackwell-ultra-b300-1-5x-faster-than-b200-with-288gb-hbm3e-and-15-pflops-dense-fp4
[5] https://www.reddit.com/r/LocalLLaMA/comments/1jedlum/dgx_sparks_nvidia_digits/
[6] https://www.theverge.com/news/631835/nvidia-blackwell-ultra-ai-chip-gb300
[7] https://opentools.ai/news/nvidia-unleashes-the-future-with-personal-ai-supercomputers
[8] https://www.nvidia.com/en-us/products/workstations/dgx-station/
[9] https://www.techtarget.com/searchenterpriseai/news/366621003/Nvidia-readies-Vera-Rubin-to-replace-Blackwell
[10] https://www.youtube.com/watch?v=krBh0Von-2A
[11] https://www.bigdatawire.com/2025/03/18/nvidia-cranks-up-the-dgx-performance-with-blackwell-ultra/
[12] https://www.engineering.com/nvidia-unveils-dgx-personal-ai-supercomputers-by-grace-blackwell/
