Comprehensive Solution for Memory Optimization
A three-dimensional solution for large model memory bottlenecks:
- dynamic memory management (DMM): Enable real-time memory grooming and defragmentation by setting memory_optimize: true in config.yaml
- Block Sparse Attention: Configure the attention.block_size parameter (recommended 64-256) to reduce the video memory footprint of 20%-40%
- gradient caching technique: For generation tasks, set generation.save_memory=true to enable the gradient checkpointing technique
Implementation suggestions: 1) Monitor Mem% fluctuations in nvidia-smi; 2) Gradually reduce block_size until OOM disappears; 3) Combine with -profile_memory parameter for bottleneck analysis
This answer comes from the articleKTransformers: Large Model Inference Performance Engine: Extreme Acceleration, Flexible EmpowermentThe
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