vLLM Release 25.12
The NVIDIA vLLM Release 25.12 is made up of two container images available on NGC: vLLM.
Contents of the vLLM container
This container image contains the complete source of the version of vLLM in /opt/vllm. It is pre-built and installed in the default system Python environment (/usr/local/lib/python3.12/dist-packages/vllm) in the container image. Visit vLLM Docs to learn more about vLLM.
The NVIDIA vLLM Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration
- Please see to the CUDA section for the list of libraries inherited from the CUDA container.
- NVIDIA CUDA 13.1.0.36
- vLLM: 0.11.1
- flashinfer 0.5.2
- transformers 4.57.1
- flash-attention 2.7.4.post1
- xgrammer 0.1.25
- torch-2.10.0a0+b4e4ee81d3
Driver Requirements
Release 25.12 is based on CUDA 13.1.0. For comprehensive and up-to-date driver compatibility information, please refer to the following documentation:
- NVIDIA CUDA Compatibility Guide - Compatibility information between CUDA versions and driver releases
- CUDA Toolkit Release Notes - Driver version requirements and compatibility matrices
- NVIDIA Drivers Download - Latest NVIDIA drivers
Key Features and Enhancements
This vLLM release includes the following key features and enhancements.
- Support for
openai/gpt-oss-20bandopenai/gpt-oss-120b - Support Nemotron-Nano-V2
Announcements
- None.
Known Issues
-
vllm serve uses aggressive GPU memory allocation by default (effectively --gpu-memory-utilization≈1.0). On systems with shared/unified GPU memory (e.g. DGX Spark or Jetson platforms), this can lead to out-of-memory errors. If you encounter OOM, start vllm serve with a lower utilization value, for example:
vllm serve <model> --gpu-memory-utilization 0.7. - On DGX Spark, workloads utilizing FP8 models may fail with CUDA stream capture errors due to illegal synchronization operations in FlashInfer kernels. A fix is available in FlashInfer.