Published · 9h agoBuild3 min read
Portable Computer's harness is real engineering. The bill of materials starts at a $4,800 DGX Spark or a 24GB RTX card, which turns local agents into a hardware order.
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The engineering worth reading about here is in the harness, not the model. Perplexity says the orchestrator that maintains the agent loop is deterministic code rather than a second model: the local model proposes an action, and the orchestrator assembles context, enforces policy, and runs approved tool calls inside an OS-level sandbox that restricts processes, filesystem paths and network access [8]. If that sandbox is unavailable, the harness disables itself before it makes any tool call at all [9]. Nate Kupp, Perplexity's VP of Computer Enterprise and Infrastructure, says the harness accounted for most of the engineering work [7], and that the team had to "revisit almost everything throughout the stack" to get a smaller model planning and executing multi-step tasks [6].
Some of that work compensates for a model that cannot hold as much as its spec sheet suggests. Perplexity puts Qwen3.8-27B's context window at 260,000 tokens but says it begins to struggle past 100,000 [10], which leaves roughly 38 percent of the advertised window as the working budget [3]. Hence the small core prompt with skills loaded only as needed [10], and connectors rewritten as command-line tools rather than full Model Context Protocol definitions handed to the model [11].
The benchmark numbers deserve a second look. Holding model and hardware constant, the five-point lead over Pi on the 53-task internal bench works out to fewer than three tasks [1]. The gap that carries weight is on ParseBench-100, a 100-task set covering charts, layouts, tables, text and formatting, where Computer sits about 30 tasks ahead of Hermes and roughly 51 ahead of Pi [13][2]. Perplexity says it plans to open-source the benchmark [14]. Until that happens, this is a vendor scoring its own harness on a test it wrote.
On procurement, the two supported routes are closer together than the sticker implies. A Spark comes to no more than about 3.2 times the price of the cheapest qualifying card [4], and the card route still needs a host machine around it [2]. Neither figure is heading down: The New Stack ties the floor to RAM pricing and does not expect relief soon [5].
The escalation path is where the on-prem pitch gets tested. When the local model stalls, what comes back from the cloud is text; the cloud model never touches the device's files or tools, and the local orchestrator keeps control of execution [16]. That is a defensible design, and it also places the request to disclose at precisely the moment the user most wants the task to finish [15]. The bundled connectors for Google Drive, Gmail, Slack and GitHub already send web searches and connector calls off the box [17], and the demo in which a local CSV analysis is posted to Slack is the clearest available picture of where the machine's edge actually falls [18].
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Perplexity, in partnership with Nvidia, has taken Computer, its agentic AI assistant, to the desktop in the form of Portable Computer.
There are two main options for running Portable Computer: a DGX Spark desktop from Nvidia running DGX OS, or a more traditional PC running Ubuntu on ARM or x64 hardware with an Nvidia RTX card with at least 24GB of GPU VRAM.
An older RTX 3090 card with 24GB of VRAM currently costs well over $1,500.
The New Stack attributes the price level to 'RAMmageddon' and says this won't likely get cheaper anytime soon.
Nate Kupp, Perplexity's vice president of Computer Enterprise and Infrastructure, told The New Stack the team had to 'revisit almost everything throughout the stack'; the company reused many of Computer's capabilities but changed the harness and model configuration for local hardware, with the model still required to plan tasks, call tools, manage files and execute multi-step tasks using fewer parameters.
The reporting this story was synthesized from, earliest first. Every link goes to the original.
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