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Tiers and Runtimes

Tiers express parsing quality and cost. Small-model backends and VLM engines are configured independently; they are not tier names.

Tier Purpose Local execution
flash Fast previews, indexing, native documents Native PDF text / document parsing; Flash OCR for scans and images
basic OCR, formulas, and tables ONNX or Torch small models; no local VLM required
standard Complex layouts and high-quality parsing Small models + VLM
advanced More demanding quality requirements Standard models and runtime, with more inference computation

PDF and images support all four tiers. Office, OpenDocument, RTF, EPUB, OFD, HTML, and CSV/TSV use local Flash and whole-document parsing. Pass tier="flash" explicitly for these formats in the local Python SDK. Plain text is not parsed, but can be indexed and read by the document library.

Default selection

  • mineru-kit parse and local Python parse() default to Standard for PDFs.
  • After discovering service capabilities, the document library prefers Standard, then Basic for PDF/images. If neither is available, it returns quality_tier_unavailable rather than silently choosing Flash or uploading to the official service.
  • Advanced is explicit. Prepare models and managed service capacity as Standard; there is no separate Advanced model download.
  • Library read consumes cached results without starting a new parse. Without an explicit tier, it prefers cached Advanced, Standard, then Basic results.

Backends and engines

Environment Automatic small models Automatic VLM
Apple Silicon / MPS Torch / MPS llama.cpp
Linux / Windows, base package ONNX / CPU when no Torch accelerator runtime is available llama.cpp
Linux, accelerator and full Torch vLLM, otherwise installed LMDeploy
Windows, accelerator and full Torch LMDeploy
CPU environment ONNX / CPU llama.cpp

Automatic selection uses installed dependencies and available devices. An explicit choice with missing dependencies fails instead of silently selecting another backend. ONNX small models run on CPU; VLM device selection is independent.

Intel XPU is excluded from automatic LMDeploy selection. Linux prefers an installed XPU-compatible vLLM and otherwise falls back to llama.cpp; Windows uses llama.cpp.

model:
  small_backend: auto
  vlm:
    engine: auto

small_backend accepts auto/onnx/torch; engine accepts auto/llama-cpp/vllm/lmdeploy. macOS defaults to llama.cpp.

Resources and platform boundaries

Native text parsing needs no inference models. Basic can run on CPU. Standard / Advanced speed and memory depend on the VLM engine, input size, and concurrency. Plan for at least 16 GB of system memory for higher-throughput local deployment. NVIDIA deployments also need an engine-supported GPU, driver, and sufficient free VRAM; 8 GB is only a planning starting point, not a guarantee for every workload.

For Apple Silicon, at least 16 GB of unified memory and a direct macOS installation are recommended. Existing AMD and vendor adaptations remain on MinerU <4; this page does not extend their compatibility claims to 4.0.

See extension modules for installation and Model Source for downloads and configuration.