Optimization solutions
To address the problem of slow inference for large models, KTransformers offers the following core solutions:
- Deep kernel-level optimization: Improve computational efficiency at the CPU/GPU instruction set level through the optimization of the underlying hardware resource scheduling, with typical scenarios improving inference speed by 3-5 times.
- Multi-GPU Parallel Computing: Configure multiple GPU device indexes in config.yaml to automate computational task partitioning and result fusion, supporting near-linear acceleration ratios
- Sparse attention mechanism: Enabling the sparse attention type in the configuration file reduces the memory access overhead of 30%-50%, which is especially suitable for long text reasoning scenarios
Implementation steps: 1) select the cuda-enabled version during installation; 2) modify the hardware parameters in config.yaml; 3) test the performance under different batch_sizes
This answer comes from the articleKTransformers: Large Model Inference Performance Engine: Extreme Acceleration, Flexible EmpowermentThe
May not be reproduced without permission:AI productivity tools " How to solve the bottleneck of slow inference for large models?
