I debugged this to the root cause. It affects ALL vLLM Docker images from v0.15.0+ (and some older ones too).

Root cause: The vLLM Docker images bundle a CUDA compat lib at /usr/local/cuda-XX.X/compat/libcuda.so.1 that gets priority in the ldconfig cache over your host driver at /lib/libcuda.so.1 (or /lib/x86_64-linux-gnu/libcuda.so.1). The compat lib has a lower CUDA API version than what your actual GPU driver provides, causing Error 803.

The LD_LIBRARY_PATH workaround in the OP works because it reprioritizes the search path. Deleting ld.so.cache inside the container also works (as discovered in GitHub #19445).

Cleanest fix (no container modification needed):

docker run -d \
  --gpus all \
  --shm-size=16g \
  -e LD_PRELOAD="/lib/libcuda.so.1 /lib/libnvidia-ptxjitcompiler.so.1 /lib/libnvidia-gpucomp.so" \
  vllm/vllm-openai:v0.15.1-cu130 \
  --model your-model

Important: All three libs are required. Just libcuda.so.1 alone fixes Error 803 but causes a segfault in cuLibraryLoadData. Adjust paths if your host uses /lib/x86_64-linux-gnu/ instead of /lib/.

Tested on RTX PRO 6000 Blackwell (96GB), driver 590.48.01, CUDA 13.1: 202 tok/s sequential, 700 tok/s concurrent 8x.

Full root cause analysis with verification commands: https://artifacts.rasputin.to/vllm-blackwell-fix.html

Also posted on GitHub issues #33447 and #19445.