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      <title>Deploying Hugging Face models to NVIDIA Triton on Kubernetes — end-to-end walkthrough</title>
      <dc:creator>olaf w</dc:creator>
      <pubDate>Sat, 25 Jul 2026 08:02:43 +0000</pubDate>
      <link>https://dev.to/olaf__99a56105ff7109/deploying-hugging-face-models-to-nvidia-triton-on-kubernetes-end-to-end-walkthrough-12ie</link>
      <guid>https://dev.to/olaf__99a56105ff7109/deploying-hugging-face-models-to-nvidia-triton-on-kubernetes-end-to-end-walkthrough-12ie</guid>
      <description>&lt;p&gt;I’ve been working on Triton Control, an open-source project that simplifies deploying models to NVIDIA Triton Inference Server.&lt;/p&gt;

&lt;p&gt;This article walks through two complete workflows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Deploying an existing Triton model repository&lt;/li&gt;
&lt;li&gt;Converting a Hugging Face DistilBERT model to TorchScript and serving it with Triton&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It covers S3-backed model repositories, Kubernetes deployment, GPU inference, and endpoint testing.&lt;/p&gt;

&lt;p&gt;I’m particularly interested in feedback on the architecture and deployment workflow.&lt;/p&gt;

&lt;p&gt;Article: &lt;a href="https://medium.com/@owilken/from-model-to-inference-endpoint-with-nvidia-triton-and-triton-control-7636439a8f28" rel="noopener noreferrer"&gt;https://medium.com/@owilken/from-model-to-inference-endpoint-with-nvidia-triton-and-triton-control-7636439a8f28&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;GitHub:  &lt;a href="https://github.com/ai-lab-tech/triton-control" rel="noopener noreferrer"&gt;https://github.com/ai-lab-tech/triton-control&lt;/a&gt;&lt;/p&gt;

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      <category>ai</category>
      <category>devops</category>
      <category>kubernetes</category>
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