<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>CUDA on The Coders Blog</title><link>https://thecodersblog.com/tag/cuda/</link><description>Recent content in CUDA on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 08 May 2026 08:25:06 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/cuda/index.xml" rel="self" type="application/rss+xml"/><item><title>[Clinical AI]: MedQA Fine-Tuning on AMD ROCm, Bypassing CUDA</title><link>https://thecodersblog.com/medqa-fine-tuning-clinical-ai-on-amd-rocm-without-cuda-2026/</link><pubDate>Fri, 08 May 2026 08:25:06 +0000</pubDate><guid>https://thecodersblog.com/medqa-fine-tuning-clinical-ai-on-amd-rocm-without-cuda-2026/</guid><description>&lt;p&gt;The digital revolution in healthcare, particularly the burgeoning field of clinical AI, has been largely defined by a singular, powerful ecosystem: NVIDIA&amp;rsquo;s CUDA. This proprietary platform has been the undisputed king, powering the vast majority of deep learning research, training, and deployment. But what if the future of specialized AI, like understanding complex medical queries, doesn&amp;rsquo;t have to be tethered to a single vendor? The MedQA project, by successfully fine-tuning the Qwen3-1.7B model on the MedMCQA dataset using AMD&amp;rsquo;s MI300X accelerators and its open-source ROCm platform, offers a compelling glimpse into a democratized AI future, one that actively bypasses the CUDA gatekeepers.&lt;/p&gt;</description></item></channel></rss>