> This page is for Boltz-2, version 1.6.0. > For other versions, use one of these documentation indexes: > - 1.10.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/boltz2/1.10.0/llms.txt > - 1.9.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.9.0/llms.txt > - 1.8.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.8.0/llms.txt > - 1.7.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.7.0/llms.txt > - 1.6.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.6.0/llms.txt > - 1.5.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.5.0/llms.txt > - 1.4.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.4.0/llms.txt > - 1.3.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.3.0/llms.txt > - 1.2.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.2.0/llms.txt > - 1.1.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.1.0/llms.txt > - 1.0.0: https://docs.nvidia.com/nim/bionemo/boltz2/1.0.0/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server. # 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: ```bash 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 (UUID: GPU-b404a1a1-d532-5b5c-20bc-b34e37f3ac46) GPU 1: NVIDIA GeForce RTX 3080 (UUID: GPU-b404a1a1-d532-5b5c-20bc-b34e37f3ac46) ``` Refer to the [NVIDIA Container Toolkit documentation](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html#gpu-enumeration) for more instructions. ## Environment Variables 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 which MUST match `NIM_HTTP_API_PORT` (or `9000` if not set). 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 via 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 | `true` | Controls whether to use TF32 for diffusion inference for improved performance on NVIDIA GPUs equipped with tensor cores. | | `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` | Controls telemetry collection. Set to `0` to disable telemetry (default), set to `1` to enable telemetry. Telemetry helps NVIDIA improve performance, compatibility, and reliability while maintaining strict privacy protections. For more information, refer to [NVIDIA's Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) and [NIM Telemetry Settings](https://developer.nvidia.com/docs/nim-tools/latest/templates/nimlib/nim-telemetry.html). | | `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). | ## 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: ```bash export LOCAL_NIM_CACHE=~/.cache/nim export NGC_API_KEY= ``` ```bash 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/boltz2:1.6.0 ``` ### Example: Debug Logging for Troubleshooting To enable verbose logging for troubleshooting issues, set the log levels to their most detailed settings: ```bash export LOCAL_NIM_CACHE=~/.cache/nim export NGC_API_KEY= ``` ```bash 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/boltz2:1.6.0 ```