Long Document Processing Memory Optimization Guide
Memory consumption for 128K contexts can be significantly reduced by:
- Enabling Context Caching: Avoid double counting of the same content, set after the first load
cache_context=TrueParameters:model.chat(tokenizer, '总结上一段的核心观点', cache_context=True) - Segmentation technology: Use a sliding window policy for very long documents:
- Use PyMuPDF to split PDF by chapter (≤32K tokens per paragraph)
- utilization
yarnExtension technology maintains inter-paragraph linkages - Final request for model integration analysis results
- Hardware-level optimization::
- Support for dynamic batch processing using the vLLM inference engine
- Enabling FlashAttention-2 Accelerates Attention Computing
- configure
--limit-mm-per-prompt '{"text":64}'Limit memory spikes
Test case: when processing 100 pages of legal contracts, the segmentation strategy can reduce the video memory occupation from 48GB to 22GB. we recommend the GLM-4.5-Air + INT4 quantization combination, which can complete the analysis of million-word documents on a 16GB video memory device.
This answer comes from the articleGLM-4.5: Open Source Multimodal Large Model Supporting Intelligent Reasoning and Code GenerationThe
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