Lightweight Deployment Program
For consumer-grade hardware environments, a portfolio optimization strategy can be used:
- Precise allocation of resources: Set the vram/dram limit in config.yaml (e.g. 24GB VRAM + 150GB DRAM) and the system will automatically perform memory swap and compute offloads
- CPU-GPU synergy: When sparse attention is enabled, the framework intelligently allocates part of the computation to CPU execution, reducing the peak memory usage.
- Layered loading mechanism: Implement on-demand loading of model parameters via model.init(partial_load=True) to support models running larger than physical memory
Recommended configuration: 1) Windows needs to enable GPU shared memory; 2) Linux recommends setting swappiness=10; 3) Mac platforms prioritize the use of MPS backend
This answer comes from the articleKTransformers: Large Model Inference Performance Engine: Extreme Acceleration, Flexible EmpowermentThe
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