Perplexity 发布 Portable Computer：本地运行
Perplexity Ships Portable Computer on NVIDIA DGX Spark: Local Harness, OS-Enforced Sandbox, and Zero Per-Token Cost for Local Steps
做本地化 Agent 部署的同学值得关注：Perplexity 把完整 agent 栈打包到 DGX Spark 上，还给出了混合升级的成本与性能权衡数据，可以对照自己的场景评估是否值得入手。
Perplexity has released Portable Computer, a local-first build of its agentic Computer platform that runs the agent harness, orchestrator, planner, tool router and post-trained models directly on NVIDIA DGX Spark. The local model, inference engine, tool sandbox and app connectors ship as one packaged system, every task begins on the device, and work handled by local models carries no per-token charge. When a step needs the live web or frontier reasoning, the orchestrator stops and asks before sending that single step to one of 15+ cloud models.
Perplexity发布了Portable Computer，这是其代理型Computer平台的本地优先构建版本，直接在NVIDIA DGX Spark上运行代理框架、编排器、规划器、工具路由器和后训练模型。本地模型、推理引擎、工具沙箱和应用连接器作为一个打包系统交付，每个任务都在设备上开始，由本地模型处理的工作不收取按令牌计费。当某个步骤需要实时网络或前沿推理时，编排器会停止并询问，然后才将该单个步骤发送到15多个云模型之一。
Is it deployable?
它是否可部署？
Yes, with a hard hardware gate. This is shipping software, not a preview binary, but it needs a GB10-class box or an RTX GPU with 24 GB of VRAM under the desk.
是的，但有严格的硬件门槛。这是正式发布的软件，不是预览版二进制文件，但它需要GB10级别的设备或配备24 GB显存的RTX GPU放在桌下。
- Company level: Enterprises and mid-market teams that already own NVIDIA workstations, plus well-funded AI-native startups. Not viable for general SMBs — the machine is the price of entry.
- Industries: Finance, legal, healthcare, government and defense, and IP-heavy engineering — anywhere data residency or contractual confidentiality blocks cloud inference.
- Applications: Fee and disclosure review across document sets, PII-bounded research, repo-scale migrations, batch summarization of local corpora, and PR triage that ends in Slack.
- 公司层面：已拥有NVIDIA工作站的企业和中型市场团队，以及资金充足的AI原生初创公司。对一般中小企业不可行——机器本身就是入场成本。
- 行业：金融、法律、医疗保健、政府和国防，以及知识产权密集型工程——任何数据驻留或合同保密性阻止云推理的领域。
- 应用场景：跨文档集的费用和披露审查、受PII约束的研究、仓库规模迁移、本地语料库的批量摘要，以及以Slack结束的PR分类。
What actually ships on the device
设备上实际交付的内容
Portable Computer is not a local chat app with a file picker. Perplexity packages the local model, inference engine, agent harness, tool sandbox and app connectors as one system, which removes the usual work of standing up an inference server and wiring tools by hand. Users select either Qwen 3.8 27B or PPLX 27B — Perplexity’s post-trained variant tuned for its own harness — with NVIDIA Nemotron 3.5 Lightning, an open 30B MoE model, listed as coming soon. Bring-your-own model and inference server is also supported.
Portable Computer不是带有文件选择器的本地聊天应用。Perplexity将本地模型、推理引擎、代理框架、工具沙箱和应用连接器打包为一个系统，消除了通常需要手动搭建推理服务器和连接工具的繁琐工作。用户可以选择Qwen 3.8 27B或PPLX 27B——Perplexity针对其自身框架调优的后训练变体——以及NVIDIA Nemotron 3.5 Lightning（一个开放的30B MoE模型），后者被列为即将推出。也支持自带模型和推理服务器。
Code and tool calls execute inside an OS-enforced sandbox that restricts processes, filesystem paths and network access. If the sandbox is unavailable, tool execution is disabled rather than silently downgraded. Gmail, Outlook, Slack and GitHub connectors route through the local orchestrator.
代码和工具调用在操作系统强制执行的沙箱内运行，该沙箱限制进程、文件系统路径和网络访问。如果沙箱不可用，工具执行将被禁用，而不是静默降级。Gmail、Outlook、Slack和GitHub连接器通过本地编排器路由。
The escalation gate is the actual design decision
升级门控是实际的设计决策
Local-first is not local-only. When a step needs the live web or frontier reasoning, the orchestrator stops and asks. Before any call, the harness selects the relevant context, runs a PII classifier over it, and shows the user exactly what would leave the machine. The approved step routes to one of 15+ cloud models; the remote adviser returns text guidance and never receives direct access to local files, tools or the conversation.
本地优先并非仅限本地。当某个步骤需要实时网络或前沿推理时，编排器会暂停并询问。在任何调用之前，工具集会选择相关上下文，运行PII分类器，并向用户展示确切会离开机器的内容。批准的步骤路由到15多个云模型之一；远程顾问返回文本指导，且从不直接访问本地文件、工具或对话。
Perplexity also engineered around small-model context limits. Qwen 3.8 27B advertises a 260K-token window but degrades past roughly 100K, so the harness keeps the system prompt and toolset small, loads specialized skills on demand, exposes connectors as compact CLI tools instead of full MCP definitions, and compacts stale context mid-run.
Perplexity还针对小模型上下文限制进行了工程设计。Qwen 3.8 27B宣称有260K令牌窗口，但超过约100K后性能下降，因此工具集保持系统提示和工具集精简，按需加载专业技能，将连接器暴露为紧凑的CLI工具而非完整的MCP定义，并在运行中压缩过时上下文。
Benchmarks
基准测试
On its 53-task Local Knowledge Work Bench — spanning deep research, financial analysis and document creation, which Perplexity says it plans to open-source — Computer running Qwen 3.8 27B on a DGX Spark scored 82.6%, against 77.6% for the open-source Pi harness and 74.0% for Hermes on the identical model. PPLX 27B raised it to 85.4%.
在其53项任务的本地知识工作台（涵盖深度研究、财务分析和文档创建，Perplexity表示计划开源）上，运行Qwen 3.8 27B的Computer在DGX Spark上得分82.6%，而开源Pi工具集在同一模型上得分为77.6%，Hermes为74.0%。PPLX 27B将其提升至85.4%。
On BrowseComp, Computer hit 66.7% versus 50.2% (Pi) and 43.9% (Hermes), using 51% less wall time and 70% fewer tokens than Pi. On ParseBench-100 for visual document understanding, it scored 65.1% against 34.6% and 13.9%.
在BrowseComp上，Computer达到66.7%，而Pi为50.2%，Hermes为43.9%，使用的时间比Pi少51%，令牌少70%。在用于视觉文档理解的ParseBench-100上，它得分65.1%，而其他分别为34.6%和13.9%。
The most informative result is the hybrid one. On Terminal Bench 2.1, the fully local run scored 59.6% at effectively zero marginal cost; adviser escalation lifted it to 73.0% at roughly $0.415 per rollout, compared with 82.4% at about $0.65 for Claude Opus 5 alone. Escalation narrows the gap to frontier models without closing it.
最有信息量的结果是混合模式。在Terminal Bench 2.1上，完全本地运行得分59.6%，边际成本几乎为零；顾问升级将其提升至73.0%，每次推出约0.415美元，而仅使用Claude Opus 5则得分82.4%，成本约0.65美元。升级缩小了与前沿模型的差距，但并未完全弥合。
Hardware, pricing model and limits
硬件、定价模式和限制
DGX Spark installs need the GB10 superchip, 128 GB of memory and at least 1 TB of storage. The Qwen 3.8 27B orchestrator ships at 3-bit quantization, a 17.4 GB download, requiring 32 GB RAM; Nemotron 3.5 Lightning is 4-bit, 19 GB, requiring 36 GB. Other systems need DGX OS or Ubuntu on ARM or x64 with an RTX GPU carrying 24 GB or more of VRAM. Installation is a standard apt repository add.
DGX Spark安装需要GB10超级芯片、128 GB内存和至少1 TB存储。Qwen 3.8 27B编排器以3位量化发布，下载大小为17.4 GB，需要32 GB RAM；Nemotron 3.5 Lightning为4位，19 GB，需要36 GB。其他系统需要DGX OS或Ubuntu（ARM或x64），并配备24 GB或更多显存的RTX GPU。安装是标准的apt仓库添加。
Availability is Linux-first for Pro, Max, Enterprise Pro and Enterprise Max subscribers; Windows follows in September, and macOS is not on the roadmap. Only one DGX Spark is supported at launch — clustering is roadmap, not shipped. Work handled by local models carries no per-token charge, which is what makes repo-scale migrations and long verification loops economically sane on owned hardware.
可用性方面，Pro、Max、Enterprise Pro和Enterprise Max订阅用户优先支持Linux；Windows版本将于9月推出，macOS不在路线图中。发布时仅支持一个DGX Spark——集群是路线图，而非已交付功能。本地模型处理的工作不收取每令牌费用，这使得在自有硬件上进行仓库级迁移和长验证循环在经济上可行。
Key Takeaways
关键要点
- Portable Computer runs the full agent harness, orchestrator and sandbox locally on DGX Spark — not just a local LLM.
- Every step starts on-device; escalation to 15+ cloud models requires explicit per-step approval after a PII check.
- Perplexity’s own tests: 85.4% with PPLX 27B on its 53-task bench, versus 77.6% for Pi on the same model.
- Terminal Bench 2.1: 59.6% local, 73.0% with adviser at ~$0.415/rollout, against 82.4% at ~$0.65 for Opus 5 alone.
- 便携式计算机在DGX Spark上本地运行完整的代理框架、编排器和沙箱——不仅仅是本地LLM。
- 每一步都在设备上启动；升级到15+云模型需要经过PII检查后的明确逐步批准。
- Perplexity自己的测试：在其53任务基准上，PPLX 27B达到85.4%，而同一模型上的Pi为77.6%。
- Terminal Bench 2.1：本地59.6%，带顾问73.0%，每次推出约$0.415，而仅Opus 5为82.4%，约$0.65。
Check out the Perplexity Portable Computer and NVIDIA local AI blog.
查看Perplexity便携式计算机和NVIDIA本地AI博客。
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文章《Perplexity在NVIDIA DGX Spark上推出便携式计算机：本地框架、操作系统强制沙箱和本地步骤零每令牌成本》首次出现在MarkTechPost上。
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