NVIDIA
cosmos-embed1
Container
NVIDIA
cosmos-embed1

Cosmos Embed1 NIM is an NVIDIA Inference Microservice (NIM) that provides access to the **Cosmos-Embed1** joint video-text embedding model via a standard HTTP API.

LayerLabelCreated
sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ENTRYPOINT
["/bin/bash" "-c" "$SERVER_START_SCRIPT_PATH"]
04/21/2026 9:21 PM UTC
sha256:cf0cd1b8a4865160763be7cffa33afc8249c6db296920ece1f382b660aa22030RUN
RUN |3 TELEMETRY_MODE=0 TELEMETRY_ENDPOINT=0 PIP_INDEX_URL=https://urm.nvidia.com/artifactory/api/pypi/nv-shared-pypi/simple /bin/sh -c touch $SERVER_START_SCRIPT_PATH &&
  chmod a+rx $SERVER_START_SCRIPT_PATH &&
  cat > $SERVER_START_SCRIPT_PATH <<-EOF #!/usr/bin/env bash set -eu # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: LicenseRef-NvidiaProprietary # L40 GPU fix: nimlib maps "L40" to "l4" due to substring matching, so we force the L40S # TensorRT profile. L40 and L40S share the same Ada Lovelace architecture (sm_89). if [ -z "\${NIM_MODEL_PROFILE+x}" ]; then GPU_NAME=\$(nvidia-smi --query-gpu=name --format=csv,noheader 2>/dev/null | head -1 ||
  echo "") if echo "\$GPU_NAME" | grep -qi "L40S"; then export PIPELINE_GPU_SKU=\${PIPELINE_GPU_SKU:-l40s} export NIM_MODEL_PROFILE=461089b6381f768a0b7e5f3a6e0e5080128ec95fbeff9849b4b2523c4c74e24f echo "Detected NVIDIA L40S GPU - using L40S TensorRT profile" elif echo "\$GPU_NAME" | grep -qi "L40"; then export PIPELINE_GPU_SKU=\${PIPELINE_GPU_SKU:-l40s} export NIM_MODEL_PROFILE=461089b6381f768a0b7e5f3a6e0e5080128ec95fbeff9849b4b2523c4c74e24f echo "Detected NVIDIA L40 GPU - using L40S TensorRT profile (same architecture)" fi fi # Test CUDA IPC functionality if [ -z "\${USE_CUDA_IPC+x}" ]; then if python3 "$NIM_DIR_PATH/test_cuda_ipc.py" >/dev/null; then export USE_CUDA_IPC=1 else export USE_CUDA_IPC=0 fi else export USE_CUDA_IPC fi echo "Using CUDA IPC: \${USE_CUDA_IPC}" # MPS: set pipe directory BEFORE starting daemon so daemon and clients use same path if [ "\${ENABLE_CUDA_MPS:-0}" = "1" ]; then export CUDA_MPS_PIPE_DIRECTORY=\${CUDA_MPS_PIPE_DIRECTORY:-/tmp/nvidia-pipe} if output=\$(nvidia-cuda-mps-control -d 2>&1); then echo "Success: MPS available (pipe dir: \$CUDA_MPS_PIPE_DIRECTORY)" ps -ef | grep mps # Disable CUDA IPC under MPS: cudaEventSynchronize() deadlocks under MPS export USE_CUDA_IPC=0 echo "MPS enabled: Forcing USE_CUDA_IPC=0" else echo "MPS not available" fi else echo "CUDA MPS not enabled (ENABLE_CUDA_MPS=${ENABLE_CUDA_MPS:-0})" fi start_server EOF # buildkit
04/21/2026 9:21 PM UTC
sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4ENV
SERVER_START_SCRIPT_PATH=/opt/nim/start_server.sh
04/21/2026 9:21 PM UTC
sha256:d126d3b3b56de8f6a07152e84001b3aa09199e9a2dca82086a654dcb40e1b757COPY
model_manifest.yaml /opt/nim/etc/default/model_manifest.yaml
04/21/2026 9:21 PM UTC
sha256:a3ed95caeb02ffe68cdd9fd84406680ae93d633cb16422d00e8a7c22955b46d4USER
nvs:1000
04/21/2026 9:21 PM UTC
sha256:4cb36360acf458e17e1679e47dbd6395edd9760884cea5ef639a8e445964ba12RUN
TELEMETRY_MODE=0 TELEMETRY_ENDPOINT=0 PIP_INDEX_URL=https://urm.nvidia.com/artifactory/api/pypi/nv-shared-pypi/simple [ -d /etc/ld.so.conf.d ] ||
  mkdir -p /etc/ld.so.conf.d &&
  [ -f /etc/ld.so.conf.d/nvidia.conf ] ||
  touch /etc/ld.so.conf.d/nvidia.conf &&
  echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf &&
  echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf &&
  echo "/usr/local/cuda/lib64" >> /etc/ld.so.conf.d/nvidia.conf &&
  if command -v ldconfig >/dev/null 2>&1; then ldconfig; fi
04/21/2026 9:21 PM UTC
sha256:400a185b2c41e25a337780b1267fe707faaafacc7d4460c2b5d5432b7498c147RUN
TELEMETRY_MODE=0 TELEMETRY_ENDPOINT=0 PIP_INDEX_URL=https://urm.nvidia.com/artifactory/api/pypi/nv-shared-pypi/simple groupadd --gid 1000 --non-unique nvs &&
  useradd --create-home --shell /usr/sbin/nologin --uid 1000 --non-unique --gid 1000 nvs &&
  chown 1000.1000 $NIM_DIR_PATH
04/21/2026 9:21 PM UTC
sha256:1182473dfa091efe4bcfd825f362c9317b1ba968fab7270635e30dfc4d3abd12COPY
./test_cuda_ipc.py /opt/nim
04/21/2026 9:21 PM UTC
sha256:90ecc06773e6fadc160e6fe27504994f5b8a8066e3f3fff1380fe304e9a24fdcCOPY
./LICENSE ./VERSION ./Acknowledgements.txt /opt/nim
04/21/2026 9:21 PM UTC
sha256:edafef4cb0a829f69f1096c9fdf133fbbfc26ccbeab173462c2ff283cf3079d7COPY
./metrics_helper.py /opt/nim
04/21/2026 9:21 PM UTC
...

NVIDIA uses cookies to improve your experience on our web site. We and our third-party partners also use cookies and other tools to collect and record information you provide as well as information about your interactions with our websites for performance improvement, analytics, and to assist in marketing efforts. By clicking "Accept All", you consent to our use of cookies and other tools as described in our Cookie Policy. You can manage your cookie settings by clicking on "Manage Settings." By continuing to use this site or by clicking one of the buttons below, you agree to our Terms of Service (which contains important waivers). Please see our Privacy Policy for more information on our privacy practices.