Update src/collections/blog/2025/03-27-docker-model-runner/index.mdx

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Alex Quinn <227241865+alexquincy@users.noreply.github.com>

Author: alexquincy

src/collections/blog/2025/03-27-docker-model-runner/index.mdx

@@ -41,7 +41,7 @@ Docker Model Runner aims to integrate local AI model execution seamlessly into t
    While the "Docker" name might imply traditional containerization for the model itself, Model Runner takes a different architectural path for performance. It facilitates running models like ai/llama3.2:1B-Q8\_0 or hf.co/bartowski/Llama-3.2-1B-Instruct-GGUF via commands such as docker model pull and docker model run. The key is that the inference itself often runs as a host-native process (initially leveraging llama.cpp), interacting with Docker Desktop or a Model Runner plugin. This design choice, which we'll explore in detail later, prioritizes direct hardware access.  
 2. Performance through Host-Native Execution & GPU Access:  
    To tackle the performance demands of LLMs, Model Runner enables the inference engine to directly access host resources. For macOS users with Apple Silicon, this means direct Metal API utilization for GPU acceleration. Windows GPU support is also on the roadmap. This approach aims to minimize the overhead often associated with virtualized GPU access in containerized environments, offering a potential speed advantage for local development.  
-3. OpenAPI-Compatible API for Seamless Integration:  
+3. OpenAI-Compatible API for Seamless Integration:  
    One of the most significant engineering benefits is the provision of an OpenAI-compatible API. This allows you to reuse existing codebases, SDKs (like LangChain or LlamaIndex), and tools with minimal, if any, modification. For many, transitioning to a local model might be as simple as changing an API endpoint URL, drastically reducing the integration effort and learning curve.  
 4. Standardized Model Management with OCI Artifacts:  
    Docker Model Runner treats AI models as Open Container Initiative (OCI) artifacts. This is a strategic move towards standardizing model distribution, versioning, and management, aligning it with the mature ecosystem already in place for container images. This opens the door to leveraging existing container registries and CI/CD pipelines for models, a crucial step towards robust MLOps practices. We'll dedicate our next post to a deep dive into this OCI integration.