<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Triton on SoloSoft</title><link>https://www.solosoft.dev/es/tags/triton/</link><description>Recent content in Triton on SoloSoft</description><generator>Hugo</generator><language>es-es</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/es/tags/triton/index.xml" rel="self" type="application/rss+xml"/><item><title>NVIDIA Triton: Servidor de Inferencia de Modelos IA Multi-Framework</title><link>https://www.solosoft.dev/es/post/triton-inference-server-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/es/post/triton-inference-server-2026/</guid><description>&lt;p&gt;Training machine learning models has become accessible to a broad audience of developers and organizations. Serving those models in production — reliably, at scale, with predictable latency and efficient resource utilization — remains a specialized engineering challenge. The gap between a trained model file and a production inference endpoint is filled with infrastructure concerns: request routing, load balancing, GPU scheduling, batching, monitoring, and failover.&lt;/p&gt;
&lt;p&gt;NVIDIA Triton Inference Server is designed to close this gap. It is a production-grade inference server that handles the complexities of model serving across multiple frameworks, hardware configurations, and deployment patterns. Think of it as the Kubernetes of model inference — not for training, but for serving models once they are trained, at any scale, with production reliability.&lt;/p&gt;</description></item></channel></rss>