What makes GB10 different

NVIDIA GB10 combines a Blackwell GPU and a 20-core Arm CPU in one Grace Blackwell superchip. Its 128GB pool is coherent unified system memory, not 128GB of discrete GPU VRAM. That shared memory design gives AI workloads room for larger models and context while reducing copies between CPU and GPU memory.

On Vast.ai, GB10 is a useful fit when you need to test an Arm-native CUDA environment or want more memory headroom than a conventional consumer GPU. The live price, recent market history, and availability shown above come from the VAST pricing directory, so this page does not freeze a rate that will become stale.

Workloads that fit GB10

• LLM and agent inference: Run quantized language models, retrieval pipelines, and autonomous-agent services that benefit from a large shared memory pool.
• Fine-tuning and prototyping: Develop, validate, and tune models before moving a proven workload to a larger accelerator or multi-GPU environment.
• Data science and edge development: Exercise CUDA, RAPIDS, robotics, and computer-vision workflows on the same Grace Blackwell and Arm platform used by DGX Spark-class systems.

Choosing GB10 on Vast.ai

GB10 favors memory capacity, platform experimentation, and power efficiency over the raw high-bandwidth-memory throughput of datacenter accelerators. Check the image, driver, storage, networking, and verification details on each offer before renting; those host-level attributes can matter as much as the processor name.

Compare current GPU pricing, explore the Vast.ai GPU Cloud, or browse the Model Library. For higher-throughput Blackwell options, compare B200. For professional graphics and AI on a discrete Blackwell GPU, see RTX PRO 5000.