No API Keys, No Cloud Bills: Running a Document Pipeline Entirely On-Device

说明与用途

文章描述了在 Mac mini 上使用 Mano‑P 模型进行本地文档处理，消除 API 密钥和云调用，确保隐私合规并节省成本。

问题

Clients with sensitive due‑diligence documents cannot upload data to cloud vision APIs due to compliance restrictions.

结果

On a Mac mini with 32 GB RAM, the 4B quantized model achieved a 56 % pass rate on 100 real GUI tasks, outperforming the cloud Qwen3‑VL‑Plus (39 % pass rate) and reducing average step time from 10.2 s to 7.9 s. The local setup eliminated network traffic, cut compliance review time from two months to two days, and saved roughly $800 per batch of 200 contracts.

工作流

• 1. User drops a task to process a batch of due diligence files.
• 2. Agent searches local filesystem for the target archive and finds it in nested folders.
• 3. Agent retrieves the password from a local note and unzips the archive.
• 4. Agent reads contents, including scanned images, using a 4B vision model.
• 5. Agent performs batch extraction on each contract, maintaining a checklist to track progress.
• 6. Results are stored locally and audited via a local log file.

证据

We run a document processing pipeline on a Mac mini. No API keys. No cloud inference. No network calls at all during execution.

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The agent handles it step by step, all on the local machine.

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Local 4B thinking model: 56% pass rate, average 7.9 seconds per step. Cloud Qwen3‑VL‑Plus: 39% pass rate, average 10.2 seconds per step.

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Zero outbound connections during the entire processing run. The audit log is a local file they can inspect.

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Mac mini, M4 chip, 32GB RAM. Total hardware cost under $1500. The alternative was a cloud vision API that would have billed us per image per page per document.

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