# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # This file represents the default ARG values of Dockerfiles generated # by render.py. These are the recommended default values for users and # is the source of truth for the values used in our delivered images. # # Some ARGs have multiple valid values and can be changed for local testing, # you can do so locally in this file, or pass the --build-arg into docker build # when building. dynamo: cuda12.9: base_image: nvcr.io/nvidia/cuda-dl-base base_image_tag: 25.06-cuda12.9-devel-ubuntu24.04 cuda13.0: base_image: nvcr.io/nvidia/cuda-dl-base base_image_tag: 25.11-cuda13.0-devel-ubuntu24.04 epp_image: us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/epp:v0.5.1 frontend_image: nvcr.io/nvidia/base/ubuntu:noble-20250619 planner_build_image: python planner_build_image_tag: 3.12-slim planner_runtime_image: nvcr.io/nvidia/distroless/python planner_runtime_image_tag: 3.12-v4.0.3 python_version: "3.12" nats_version: v2.10.28 etcd_version: v3.5.21 nixl_ref: 0.10.1 nixl_ucx_ref: v1.20.x nixl_gdrcopy_ref: v2.5.1 nixl_ucx_efa_ref: 9d2b88a1f67faf9876f267658bd077b379b8bb76 nixl_libfabric_ref: v2.3.0 enable_kvbm: "true" enable_media_ffmpeg: "false" enable_gpu_memory_service: "true" ffmpeg_version: "8.1" efa_version: 1.47.0 vllm: cuda12.9: base_image: nvcr.io/nvidia/cuda-dl-base runtime_image: nvcr.io/nvidia/cuda base_image_tag: 25.06-cuda12.9-devel-ubuntu24.04 runtime_image_tag: 12.9.1-runtime-ubuntu24.04 vllm_ref: v0.19.1 cuda13.0: base_image: nvcr.io/nvidia/cuda-dl-base runtime_image: nvcr.io/nvidia/cuda base_image_tag: 25.11-cuda13.0-devel-ubuntu24.04 runtime_image_tag: 13.0.2-runtime-ubuntu24.04 vllm_ref: v0.19.1 xpu: base_image: intel/deep-learning-essentials runtime_image: intel/deep-learning-essentials base_image_tag: 2025.3.2-0-devel-ubuntu24.04 runtime_image_tag: 2025.3.2-0-devel-ubuntu24.04 vllm_ref: v0.17.1 cpu: base_image: ubuntu runtime_image: ubuntu base_image_tag: 24.04 runtime_image_tag: 24.04 vllm_ref: v0.16.0 flashinf_ref: v0.6.6 lmcache_ref: 0.4.2 vllm_omni_ref: "release/v0.19.0rc1" nixl_ref: 0.10.1 max_jobs: "10" enable_media_ffmpeg: "false" enable_gpu_memory_service: "true" enable_kvbm: "true" enable_modelexpress_p2p: "false" modelexpress_ref: "76fc5d7f06c37121ee8789a29fac6f9b08c4743a" # v0.3.0 sglang: cuda12.9: base_image: nvcr.io/nvidia/cuda-dl-base runtime_image: lmsysorg/sglang base_image_tag: 25.06-cuda12.9-devel-ubuntu24.04 runtime_image_tag: v0.5.10.post1-runtime cuda13.0: base_image: nvcr.io/nvidia/cuda-dl-base runtime_image: lmsysorg/sglang base_image_tag: 25.11-cuda13.0-devel-ubuntu24.04 runtime_image_tag: v0.5.10.post1-cu130-runtime # SGLang uses the NIXL stack from the upstream lmsysorg/sglang runtime image. # Do not add nixl_ref here: Dynamo does not build or install its NIXL wheel # for SGLang, and SGLang does not use Dynamo KVBM/block-manager at runtime. enable_media_ffmpeg: "true" enable_gpu_memory_service: "true" enable_kvbm: "false" trtllm: cuda13.1: base_image: nvcr.io/nvidia/pytorch runtime_image: nvcr.io/nvidia/cuda-dl-base base_image_tag: 26.02-py3 runtime_image_tag: 26.02-cuda13.1-runtime-ubuntu24.04 nixl_ref: 0.10.1 enable_media_ffmpeg: "false" enable_gpu_memory_service: "true" enable_kvbm: "true" python_version: "3.12" index_url: https://pypi.nvidia.com/ pip_wheel_dir: /tmp/trtllm_wheel/ pip_wheel: tensorrt-llm==1.3.0rc11 trtllm_wheel_image: nvcr.io/nvidia/tensorrt-llm/release:${TENSORRTLLM_PIP_WHEEL#*==} github_trtllm_commit: v1.3.0rc11 torch_version: 2.11.0a0+eb65b36914.nv26.2 torch_tensorrt_version: 2.11.0a0 torchvision_version: 0.25.0a0+1e53952f.nv26.2.44259020 torchao_ver: 0.16.0+gita89eaab2 torchdata_ver: 0.11.0 torchtitan_ver: 0.2.1+git9f211ec1 jinja2_version: 3.1.6 sympy_version: 1.14.0 pytorch_triton_ver: 3.6.0+git9844da95.nv26.2 flash_attn_version: 2.7.4.post1+nv26.2.44259020 flashinfer_python_ver: 0.6.6 has_trtllm_context: "0"