Fix RayTaskError

✅ Solution

RayTaskError in vllm often arises from GPU memory allocation issues during distributed execution, particularly with pipeline parallelism or Triton kernels. The fix involves reducing the `gpu_memory_utilization` parameter in `vllm.EngineArgs`, enabling CUDA memory manager (CMM) via environment variable `export CUDA_VISIBLE_DEVICES=[GPU_IDS]` where [GPU_IDS] uses device ID's (not rank) or carefully adjusting the model configuration (e.g., reducing `max_model_len` or n_gpu_shard values) to fit within available GPU memory, and ensuring all nodes in the cluster have compatible CUDA versions.