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NeMo Agent Toolkit (NAT) can use a model served locally by Docker Model Runner (DMR) through NAT’s OpenAI-compatible model client. For a NAT process running on your computer, point that client at http://localhost:12434/engines/v1 and set the model to DMR’s full identifier, such as ai/smollm2. You do not need a NAT-specific DMR plugin for this connection pattern, and NAT does not require a GPU by default.
How the connection works
NAT is a Python toolkit for building agents and connecting them to tools, data sources, and frameworks. Docker Model Runner is a local model-serving runtime. DMR exposes an OpenAI-compatible API, so NAT can send requests through its OpenAI-compatible client rather than through a special NAT-to-DMR integration.
The essential settings are the provider/client type, the DMR API base URL, and the exact model identifier that DMR knows. NAT’s precise YAML keys depend on the workflow and framework plugin you use. Follow the current NAT example for that workflow; do not assume one universal configuration block applies to every NAT project.
Set up NAT and Docker Model Runner
1. Install NAT in a supported Python environment
NAT supports Python 3.11, 3.12, and 3.13. Install the base package with pip install nvidia-nat or use NAT’s documented uv workflow. If your agent uses an optional framework integration, install the corresponding extra—for example, nvidia-nat[langchain] for LangChain.
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2. Enable Docker Model Runner
Docker Model Runner requires Docker Engine or Docker Desktop. On Docker Desktop, enable Model Runner in Docker’s AI settings. The current overview lists Docker Desktop 4.41 or later for Windows and 4.40 or later for macOS. On Docker Engine, install and start the runner using Docker’s instructions for your platform.
If NAT runs directly on the host, make sure DMR’s host-side TCP access is enabled so the NAT process can reach its API. A NAT process inside a container needs a route to the host’s Model Runner service instead; on Docker Desktop, the documented hostname is commonly model-runner.docker.internal.
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3. Pull a model and check that DMR can see it
For example, pull the model with Docker’s CLI:
docker model pull ai/smollm2Then check the local model status:
docker model statusYou can also query DMR’s model-list endpoint:
curl http://localhost:12434/engines/v1/modelsUse a model identifier returned by DMR. Include its namespace, as in ai/smollm2; a short name such as smollm2 may not identify the same model to the server.
Configure NAT to call the local model
For NAT running as a host process, configure its OpenAI-compatible client with these values:
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| Setting | Value | What to check |
|---|---|---|
| Provider/client | NAT’s OpenAI-compatible model client | Use the configuration supported by the NAT workflow or framework plugin you selected. |
| Base URL | http://localhost:12434/engines/v1 |
This is the local DMR API base URL for a host process. |
| Model | The full DMR identifier, for example ai/smollm2 |
Confirm the exact identifier using docker model status or /engines/v1/models. |
| API key | A placeholder such as not-needed |
DMR does not require a real API key for this local API. |
If NAT itself runs in a container on Docker Desktop, use the documented Model Runner hostname rather than assuming that localhost refers to the host. The common host name is model-runner.docker.internal; construct the base URL for the DMR API and verify reachability from inside the NAT container. A service running in a container may have a different network route on Docker Engine, so use the networking instructions for that environment.
DMR’s chat-completions endpoint is /engines/v1/chat/completions; model discovery uses /engines/v1/models, and embeddings use /engines/v1/embeddings. If the endpoint responds to the model-list request but NAT cannot complete a chat request, check that the NAT client is using the compatible API format, the full model name, and a base URL ending at /engines/v1 rather than at an individual operation endpoint.
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Choose a Docker Model Runner backend
The backend determines which model formats, hardware, and workloads are practical. Docker documents three options with different intended uses:
| Backend | Best fit | Key considerations |
|---|---|---|
| llama.cpp | A broad starting point for local inference, including CPU systems, Apple Silicon, and modest local GPUs. | DMR’s default engine; supports GGUF models and broad platform coverage. |
| vLLM | Higher-throughput serving and concurrent requests on supported NVIDIA GPU systems. | Docker documents Safetensors models for this path. Confirm the GPU, drivers, and model format are supported before choosing it. |
| Diffusers | Image generation with Diffusers models. | Docker documents an NVIDIA GPU requirement on Linux. |
For a single local agent or an initial compatibility check, llama.cpp is generally the least demanding place to start. Consider vLLM when serving throughput and concurrent requests matter and the environment meets its NVIDIA GPU and model-format requirements. Diffusers is for image-generation workloads, not a drop-in choice for a text-chat agent.
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Also weigh model size, context length, available memory, GPU-layer offload, startup time, and expected concurrency. DMR exposes options such as context size and GPU-layer offload; larger models and longer contexts consume more resources. The official sources reviewed do not publish an end-to-end NAT-plus-DMR benchmark, so backend choice should be validated against your own model and workload rather than an assumed NAT-specific performance result.
Do you need a GPU, CUDA, or NVIDIA Container Toolkit?
NAT itself is a Python library and does not require a GPU by default. The model-serving backend and chosen model determine whether acceleration is available or required. DMR on Docker Engine documents CPU, NVIDIA CUDA, AMD ROCm, and Vulkan backends, subject to platform and driver requirements; available options vary by operating system and hardware.
- CPU or broad local compatibility: Start with the llama.cpp backend and a model suited to available memory.
- NVIDIA acceleration: CUDA-capable hardware and compatible drivers are needed for the NVIDIA path. Container-level GPU access may require the NVIDIA Container Toolkit, depending on the deployment.
- NIM containers: NVIDIA’s local-LLM guidance specifies an NVIDIA GPU with CUDA support, NVIDIA Container Toolkit, and an NVIDIA API key for NIM containers. Those requirements are specific to that path, not to NAT or every DMR setup.
- Dynamo: NVIDIA’s documented Dynamo example calls for Docker, NVIDIA Container Toolkit, and compatible NVIDIA driver/CUDA support, and labels the integration experimental.
Check common connection and startup problems
DMR is not reachable
- For a host-run NAT process, check that Docker Model Runner is enabled and host-side TCP access is available.
- For a containerized NAT process, check reachability from inside that container. On Docker Desktop, use the Model Runner hostname rather than the container’s own
localhost. - Confirm the base URL points to the DMR API root,
/engines/v1, and not directly to/chat/completions.
The model is reported as missing
- Pull the model with
docker model pulland check it withdocker model status. - Use the full identifier, including its namespace, and compare it with DMR’s model-list response.
The first request is slow
DMR loads models on demand and keeps them in memory until another model is requested or an inactivity timeout is reached. Docker’s current CLI reference describes a five-minute inactivity timeout. As a result, the first request after a model is unloaded may include model-loading time; distinguish that startup delay from the response time of a model that is already loaded.
Requests fail under load or with a larger context
Check whether the selected backend is appropriate for the hardware and concurrency you need, and whether the model and context size fit available memory. Increasing context size or model size raises resource demand. A vLLM deployment may suit supported NVIDIA systems with concurrent-serving needs, while llama.cpp offers broader platform support; neither choice guarantees a particular response time for a NAT workflow.
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Protect the local API
Docker states that the Model Runner API is not authenticated by default. Keep it on a trusted local or container network, and consider exposure carefully before enabling host-side TCP access or making the service reachable beyond the machine. A placeholder API key satisfies clients that require a key field; it does not add authentication to DMR.
Quick Recap
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