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Configuring the Boltz-2 NIM#

The Boltz-2 NIM uses Docker containers. Each NIM has its own Docker container and there are several ways to configure it. The section below describes how to configure a NIM container.

GPU Selection#

By default, Docker can use all available GPUs on the system when it starts with the NVIDIA Container Runtime:

docker run --runtime=nvidia ...

In environments with a combination of GPUs, you can only expose specific GPUs inside the container using either:

The --gpus flag. For example, docker run --gpus='"device=1"' ...
The environment variable NVIDIA_VISIBLE_DEVICES. For example, to expose only Device 1, pass -e NVIDIA_VISIBLE_DEVICES=1. To expose GPU IDs 1 and 4, pass-e NVIDIA_VISIBLE_DEVICES=1,4.

The device IDs to use as inputs are listed in the output of nvidia-smi -L:

GPU 0: Tesla H100 80GB HBM3 (UUID: GPU-aaaaaaaa-bbbb-cccc-dddd-111111111111)
GPU 1: Tesla H100 80GB HBM3 (UUID: GPU-eeeeeeee-ffff-0000-1111-222222222222)

Refer to the NVIDIA Container Toolkit documentation for more instructions.

Environment Variables#

The Boltz-2 NIM container image on NVIDIA Container Registry is nvcr.io/nim/mit/boltz2:<version> (NGC org nim, team mit). Examples on this page use the substituted form nvcr.io/nim/mit/boltz2:1.7.0, which resolves to that path.

The following table describes the environment variables that can be passed into a NIM as a -e argument added to a docker run command:

ENV	Required?	Default	Notes
`NGC_API_KEY`	Yes	None	You must set this variable to the value of your personal NGC API key.
`NIM_CACHE_PATH`	No	`/opt/nim/.cache`	Location (in container) where the container caches model artifacts.
`NIM_HTTP_API_PORT`	No	`8000`	Publish the NIM service to the prescribed port inside the container. Make sure to adjust the port passed to the `-p/--publish` flag of Docker run to reflect that (ex: `-p $NIM_HTTP_API_PORT:$NIM_HTTP_API_PORT`). The left-hand side of this `:` is your host address:port, and does NOT have to match with `$NIM_HTTP_API_PORT`. The right-hand side of the `:` is the port inside the container and MUST match the port the NIM listens on: use the value of `NIM_HTTP_API_PORT` when set, otherwise 8000 (the default in this column—not a different port). Supported endpoints are `/v1/license` (Returns the license information), `/v1/metadata` (Returns metadata including asset information, license information, model information, and version) and `/v1/metrics` (Exposes Prometheus metrics using an ASGI app endpoint).
`NIM_LOG`	No	`INFO`	Controls NIM service logging. Available options are `DEBUG`, `INFO`, `WARNING`, `ERROR`, and `CRITICAL`.
`NIM_LOG_LEVEL`	No	`INFO`	Alternative NIM logging level control. Available options are `DEBUG`, `INFO`, `WARNING`, `ERROR`, and `CRITICAL`.
`TLLM_LOG_LEVEL`	No	`INFO`	Controls TensorRT-LLM backend logging. Available options are `VERBOSE`, `INFO`, `WARNING`, and `ERROR`.
`MODEL_PATH`	No	Unset	This variable enables a hard override of the NIM’s model path. Users should generally not need to use this variable, but it can be useful when deploying to some cloud services that use alternative methods for model caching.
`NIM_BOLTZ_ENABLE_DIFFUSION_TF32`	No	`1`	Integer: `1` enables TF32 for diffusion inference (default); `0` disables it. Use TF32 for improved performance on NVIDIA GPUs with Tensor Cores. Pass as `-e NIM_BOLTZ_ENABLE_DIFFUSION_TF32=1` or `=0`.
`NIM_BOLTZ_STRUCTURE_OPTIMIZED_BACKEND`	No	`trt`	Controls the backend used for the pairformer (structure prediction) module. Can be either `trt` for TensorRT or `torch` for PyTorch. If TRT is selected but not available, the model will automatically fall back to `torch`.
`NIM_DEFAULT_RANDOM_SEED`	No	`42`	Controls the random seed used for torch / trt inference.
`NIM_TELEMETRY_MODE`	No	`0`	String `0` disables telemetry (default). `1` enables baseline collection (hardware and NIM information). On RTX GPUs, collection stays off unless you also set `NIM_TELEMETRY_ENABLE_ON_RTX=true`. Refer to NIM Telemetry. For privacy and full configuration details, refer to NVIDIA’s Privacy Policy and NIM Telemetry Settings.
`NIM_TELEMETRY_ENABLE_ON_RTX`	No	`false`	String `true` allows telemetry on RTX GPUs when `NIM_TELEMETRY_MODE` is not `0`. Default `false`. Has no effect when telemetry is disabled. Refer to NIM Telemetry.
`NIM_TELEMETRY_ENABLE_LOGGING`	No	`true`	Enables logging for telemetry operations when set to `true`. Only applicable when `NIM_TELEMETRY_MODE=1`.
`NIM_EXPOSE_CONFIDENCE_SCORES`	No	`false`	Controls whether to expose confidence scores in output directories. Set to `true` to enable confidence score output persistence in NIM cache folder, set to `false` to disable (default).

NIM Telemetry#

NIM Telemetry collects minimal host and NIM information to help improve performance, reliability, and compatibility across deployments. Collection is disabled by default on RTX GPUs and can be enabled explicitly when you opt in.

`NIM_TELEMETRY_MODE`#

Controls telemetry collection mode.

0 — Disables telemetry.
1 — Enables baseline collection (hardware and NIM information).

Default: 0

`NIM_TELEMETRY_ENABLE_ON_RTX`#

Set to true to allow telemetry collection on RTX GPUs. This setting applies only when NIM_TELEMETRY_MODE is not 0 (for example, when baseline collection is enabled on the deployment).

Default: false

For what is collected, retention, and advanced options, refer to NIM Telemetry Settings and NVIDIA’s Privacy Policy.

Volumes#

The following table describes the paths inside the container into which local paths can be mounted.

Container path	Required	Notes	Docker argument example
`/opt/nim/.cache` (or `NIM_CACHE_PATH` if present)	Not required, but if this volume is not mounted, the container will do a fresh download of the model each time it is brought up.	This is the directory within which models are downloaded inside the container. It is very important that this directory can be accessed from inside the container. This can be achieved by setting the permissions of the local directory to `read-write-execute` (`777`). For example, to use `~/.cache/nim` as the host machine directory for caching models, first do `mkdir -p ~/.cache/nim`, then `chmod 777 ~/.cache/nim` before running the `docker run command`.	`-v ~/.cache/nim:/opt/nim/.cache`

Logging Configuration#

The Boltz-2 NIM provides logging environment variables to control verbosity for different components. You can configure these when starting the container to adjust the level of detail in logs for debugging or production use.

Example: Running with Logging Configuration#

The following example shows how to run the NIM with standard logging configuration:

export LOCAL_NIM_CACHE=~/.cache/nim
export NGC_API_KEY=<Your NGC API Key>

docker run --rm --name boltz2 --runtime=nvidia \
    --shm-size=16G \
    -e NGC_API_KEY \
    -e NIM_LOG=INFO \
    -e NIM_LOG_LEVEL=INFO \
    -e TLLM_LOG_LEVEL=INFO \
    -v $LOCAL_NIM_CACHE:/opt/nim/.cache \
    -p 8000:8000 \
    nvcr.io/nim/mit/boltz2:1.7.0

Example: Debug Logging for Troubleshooting#

To enable verbose logging for troubleshooting issues, set the log levels to their most detailed settings:

export LOCAL_NIM_CACHE=~/.cache/nim
export NGC_API_KEY=<Your NGC API Key>

docker run --rm --name boltz2 --runtime=nvidia \
    --shm-size=16G \
    -e NGC_API_KEY \
    -e NIM_LOG=DEBUG \
    -e NIM_LOG_LEVEL=DEBUG \
    -e TLLM_LOG_LEVEL=VERBOSE \
    -v $LOCAL_NIM_CACHE:/opt/nim/.cache \
    -p 8000:8000 \
    nvcr.io/nim/mit/boltz2:1.7.0