When specifying AI/ML library dependencies, ensure version compatibility between related packages and verify that prebuilt binaries are available for the target environment. Many AI libraries like auto_gptq, autoawq, and flash_attn are compiled against specific versions of PyTorch and CUDA, and version mismatches can force compilation from source or cause runtime failures. Before finalizing requirements, check: - Library compatibility matrices (e.g., auto_gptq 0.7.1 requires torch 2.2.1, not 2.3.1) - Availability of prebuilt wheels for your CUDA version - Whether dependencies require CUDA compilers if building from source Consider separating incompatible libraries into different requirements files when necessary. Example of problematic dependencies: ``` torch==2.3.1 auto_gptq==0.7.1 # Compiled against torch 2.2.1, will cause issues ``` Better approach: ``` # requirements-gptq.txt torch==2.2.1 auto_gptq==0.7.1 # requirements-awq.txt torch==2.4.1 autoawq==0.2.6 ```