Unsloth provides a multilevel optimization scheme for the inference session:
- Architecture-level optimization: Implemented using memory-efficient attention mechanisms, supporting acceleration techniques such as FlashAttention
- Quantitative reasoning support: In addition to 4-bit quantization for training, flexible inference precision options such as 8-bit/16-bit are also supported
- Batch optimizationDynamic Batching: Automatic implementation of Dynamic Batching, significantly increasing throughput.
- hardware adaptation: Specific kernel optimizations for different NVIDIA/AMD/Intel hardware platforms
- latency hiding technology: Reduce end-to-end response time with prefetching and pipelining techniques
In practice, developers can set theinference_mode="optimized"Parameters are enabled for a full set of optimizations, which according to tests can achieve up to a 3x improvement in inference speed. For deployment scenarios, it is recommended to combine with a dedicated inference server such as vLLM or Ollama for optimal performance.
This answer comes from the articleUnsloth: an open source tool for efficiently fine-tuning and training large language modelsThe
May not be reproduced without permission:AI productivity tools " What optimizations does Unsloth have in the model inference phase?
