The NVIDIA GB10 and GB300 Superchips are both part of NVIDIA's Blackwell architecture, but they serve different purposes and have distinct specifications.
Memory
- GB10 Superchip: This chip features 128 GB of unified coherent memory, which is shared between the CPU and GPU. This unified memory model eliminates the need for PCIe transfers between CPU and GPU, enhancing efficiency for AI workloads. The memory is LPDDR5x, which is suitable for compact, power-efficient systems like the ASUS Ascent GX10 mini-PC[1][4][7].
- GB300 Superchip: In contrast, the GB300 is designed for high-end data center applications and features significantly more memory. Each GPU in the GB300 setup includes 288 GB of HBM3e memory, which is part of a larger system that can scale up to 40 TB of fast memory across the entire rack-scale solution. This substantial memory capacity is crucial for handling large AI models and complex data center workloads[2][5][8].
Performance
- GB10 Superchip: The GB10 is optimized for AI performance on the desktop, delivering up to 1,000 TOPS (tera operations per second) of AI processing power. It includes a robust Blackwell GPU with fifth-generation Tensor Cores and an Arm-based CPU, making it suitable for running large language models and other AI tasks on a compact form factor[1][7].
- GB300 Superchip: The GB300 offers much higher performance, with each GPU providing significantly more compute power than the GB10. The GB300 is part of a larger system that can combine multiple GPUs to achieve enormous scale, with specifications indicating performance levels that far exceed those of the GB10. For instance, the GB300 NVL72 system can achieve performance metrics in the petaFLOPS range, making it ideal for massive AI workloads in data centers[2][5][8].
Architecture and Connectivity
- GB10 Superchip: The GB10 uses NVIDIA NVLink-C2C for chip-to-chip interconnects, providing a cohesive CPU-GPU memory model with high bandwidth. It is designed for compact systems and focuses on efficient AI processing for developers and researchers[1][4].
- GB300 Superchip: The GB300 incorporates the fifth-generation of NVIDIA NVLink, which is a scale-up interconnect designed to enhance performance across large systems. It also includes advanced networking capabilities like the ConnectX-8 SuperNIC, offering 800 Gbps of network connectivity per GPU. This setup is optimized for high-speed data transfer and efficient AI processing in large-scale environments[2][5].
In summary, while both chips are part of NVIDIA's Blackwell architecture, the GB10 is tailored for desktop AI development with a focus on compactness and efficiency, whereas the GB300 is designed for massive data center deployments requiring high performance and scalability.
Citations:
[1] https://www.techpowerup.com/334249/asus-unveils-new-ascent-gx10-mini-pc-powered-nvidia-gb10-grace-blackwell-superchip
[2] https://www.nvidia.com/en-us/data-center/gb300-nvl72/
[3] https://semianalysis.com/2024/12/25/nvidias-christmas-present-gb300-b300-reasoning-inference-amazon-memory-supply-chain/
[4] https://www.bigdatawire.com/2025/01/10/inside-nvidias-new-desktop-ai-box-project-digits/
[5] https://www.techpowerup.com/330154/nvidia-gb300-blackwell-ultra-will-feature-288-gb-hbm3e-memory-1400-w-tdp
[6] https://www.theregister.com/AMP/2025/03/18/gtc_frame_nvidias_budget_blackwell/
[7] https://meta-quantum.today/?p=3460
[8] https://www.tweaktown.com/news/103991/nvidia-gb300-blackwell-ultra-ai-gpu-288gb-hbm3e-1-4kw-power-50-faster-than-gb200/index.html
[9] https://www.theregister.com/2025/01/07/nvidia_project_digits_mini_pc/