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How does unified memory help NVIDIA RTX Spark™ support demanding local workloads?
It gives the CPU and GPU access to a shared pool of high-speed memory.
It transfers every workload from the CPU to the GPU.
It increases the number of CPU and CUDA cores available.
It stores AI models in the cloud until they are needed.
What distinguishes a personal AI agent from a traditional step-by-step software workflow?
It requires the user to move information manually between applications.
It can interpret a high-level goal, plan the required steps, and complete a multi-step workflow.
It can complete only one action before requiring another instruction.
It is limited to searching files and cannot interact with applications.
A company needs to analyze sensitive documents without uploading them to a public cloud AI service. Which RTX Spark™ benefit most directly addresses this requirement?
High-resolution gaming performance
Automatic access to every file on the device
Cloud-based processing of confidential documents
Local processing that can keep sensitive information within the device boundary
A video editor regularly works with high-resolution media, complex visual content, and local AI generation. Which RTX Spark™ component most directly supports these graphics and parallel-computing workloads?
Windows Taskbar integration
The NVIDIA Grace CPU
The NVIDIA Blackwell RTX GPU
Windows security primitives
Why might a software developer use an RTX Spark™-powered PC for an AI coding assistant?
It can index codebases and run AI-assisted development workflows locally without sending confidential code to an external server.
It automatically replaces the developer's existing development tools.
It requires models and repositories to be hosted in a public cloud.
It limits coding assistants to autocomplete and prevents background tasks.
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