Perplexity has introduced Portable Computer, a product that moves its full Computer Agent stack onto a user’s own machine. According to the update, model execution, planning, tool use, task queues, and file search can all run locally, which means those local tasks no longer consume Perplexity credits. The initial release supports NVIDIA DGX Spark and can run Qwen 3.8 27B as well as PPLX 27B, a model post-trained by Perplexity. When a task needs web search, browser control, or stronger reasoning, the system can call on more than 15 frontier cloud models, but only with user approval. Even so, Portable Computer is currently available only to Pro and Max subscribers. The launch has already drawn pushback from some community members, who questioned why they still need to pay a monthly Perplexity fee if they are using their own hardware and compute to run models locally.

Perplexity has introduced a portable AI computer as a local-first version of its agent platform, Computer, designed to run directly on NVIDIA DGX Spark. The system packages local models, an inference engine, a tool sandbox, and application connectors so that tasks can begin on-device, with work handled by local models generating no token-based charges. When a step needs live internet access or stronger frontier reasoning, the orchestrator pauses, asks the user, and then sends only that single step to one of more than 15 cloud models. Deployment requires GB10-class hardware or an RTX GPU with 24 GB of VRAM. Perplexity is targeting enterprises that already have NVIDIA workstations, mid-sized teams, and well-funded AI-native startups. The company says the setup fits sectors such as finance, legal, healthcare, government, and defense, where data residency or contractual confidentiality requirements are strict. In a 53-task local knowledge-work benchmark, Computer running Qwen 3.8 27B scored 82.6% on DGX Spark, ahead of the open-source Pi toolchain at 77.6% and Hermes at 74.0%. Using the PPLX 27B model pushed the score to 85.4%. In hybrid mode, fully local execution carries zero marginal cost, while an advisor upgrade can improve performance at about $0.415 per run.