Get your VLM running in 3 simple steps on Intel CPUs
Get your VLM running in 3 simple steps on Intel CPUs Get your VLM running in 3 simple steps on Intel CPUs Published October 15, 2025 Update on GitHub Upvote 22 Ezequiel Lanza ezelanza Intel Helena helenai Intel Nikita nikita-savelyev-intel Intel Ella Charlaix echarlaix Ilyas Moutawwakil IlyasMoutawwakil With the growing capability of large language models (LLMs), a new class of models has emerged: Vision Language Models (VLMs) . These models can analyze images and videos to describe scenes, create captions, and answer questions about visual content.
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- Get your VLM running in 3 simple steps on Intel CPUs Published October 15, 2025 Update on GitHub Upvote 22 Ezequiel Lanza ezelanza Intel Helena helenai Intel Nikita nikita-savelyev-intel Intel Ella Charlaix echarlaix Ilyas Moutawwakil IlyasMoutawwakil With the growing capability of large language models (LLMs), a new class of models has emerged: Vision Language Models (VLMs) .
- These models can analyze images and videos to describe scenes, create captions, and answer questions about visual content.
- While running AI models on your own device can be difficult as these models are often computationally demanding, it also offers significant benefits: including improved privacy since your data stays on your machine, and enhanced speed and reliability because you're not dependent on an internet connection or external servers.
- This is where tools like Optimum Intel and OpenVINO come in, along with a small, efficient model like SmolVLM .
- In this blog post, we'll walk you through three easy steps to get a VLM running locally, with no expensive hardware or GPUs required (though you can run all the code samples from this blog post on Intel GPUs).
- Deploy your model with Optimum Small models like SmolVLM are built for low-resource consumption, but they can be further optimized.