Hopper-1.8.3 image incompatible with CUDA 13.0 - Floating point exception on H20

### System Info Environment: - CUDA Driver Version: 550.105.08 - CUDA Version: 13.0 - GPU: NVIDIA H20 - GPU Memory: 97871 MiB (~96 GB) - Platform: OpenShift/Kubernetes - TEI Version: 1.8.3 - TEI Image: ghcr.io/huggingface/text-embeddings-inference:hopper-1.8.3 - Model: Qwen/Qwen3-Embedding-8B Launch Parameters: --model-id Qwen/Qwen3-Embedding-8B --pooling mean --max-batch-requests 128 --max-concurrent-requests 256 --max-batch-tokens 40960 --dtype float16 ### Information - [x] Docker - [x] The CLI directly ### Tasks - [x] An officially supported command - [ ] My own modifications ### Reproduction 1. Pull and run the TEI hopper image on a system with CUDA 13.0: docker run --gpus all -p 80:80 \ ghcr.io/huggingface/text-embeddings-inference:hopper-1.8.3 \ --model-id Qwen/Qwen3-Embedding-8B \ --pooling mean \ --max-batch-requests 128 \ --max-concurrent-requests 256 \ --max-batch-tokens 40960 \ --dtype float16 2. Observe the initialization logs: - Container starts and text embeddings router initializes - Attempts to load model: Qwen/Qwen3-Embedding-8B - WARNING appears: "Could not find a Sentence Transformers config" - INFO: "Maximum number of tokens per request: 40960" - INFO: "Starting 8 tokenization workers" - INFO: "Starting model backend" - INFO: "Starting FlashOwn3 model on Cuda(CudaDevice(DeviceId(1)))" - CRASH: "Floating point exception (core dumped)" 3. Check GPU status: nvidia-smi Result: Shows 0% GPU utilization, 0 MiB memory usage, no running processes 4. Result: Container fails to serve embeddings, GPU remains unused Note: The same model works correctly with the standard CUDA 12 variant: docker run --gpus all -p 80:80 \ ghcr.io/huggingface/text-embeddings-inference:1.8.3 \ --model-id Qwen/Qwen3-Embedding-8B \ --pooling mean ### Expected behavior Container should start successfully without crashes Questions: 1. Is CUDA 13.x support planned for the hopper variant? 2. Can a hopper-cuda13 image variant be provided?