The following solutions can be implemented for memory management of 512K ultra-long contexts:
- Hardware layer optimization: Configure at least 4 NVIDIA H100-80G GPUs through the
tensor-parallel-size=4Realize distributed loading of video memory. It is recommended to enable the CPU offload function for single card scenarios. - memory compression technology: Add the following to the transformers call
max_memoryparameter allocates the upper limit of memory for each device, in conjunction with thedevice_map="balanced"Automatic load balancing. - chunking strategy: For 1600-page level documents, the model is used to generate segmented summaries (1 segment per 20 pages), and then global analysis is performed based on the summaries, and the memory consumption can be reduced by 70%.
- monitoring and protection mechanism: Pre-deployment with
nvidia-smi -l 1Real-time monitoring of video memory, settingmax_split_size_mb=512Prevent memory fragmentation.
When encountering OOM errors, prioritize attempts to reducethinking_budgetvalue, or change to the8-bitQuantized version (requires additional installation of the bitsandbytes library).
This answer comes from the articleSeed-OSS: Open Source Large Language Model for Long Context Reasoning and Versatile ApplicationsThe
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