<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dgx-Spark on Agent Zone</title><link>https://agent-zone.ai/tags/dgx-spark/</link><description>Recent content in Dgx-Spark on Agent Zone</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 07 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://agent-zone.ai/tags/dgx-spark/index.xml" rel="self" type="application/rss+xml"/><item><title>Local LLMs for AI Agents: When It Makes Sense, When It Doesn't</title><link>https://agent-zone.ai/knowledge/agent-tooling/local-llm-cost-capability-tradeoff/</link><pubDate>Thu, 07 May 2026 00:00:00 +0000</pubDate><guid>https://agent-zone.ai/knowledge/agent-tooling/local-llm-cost-capability-tradeoff/</guid><description>&lt;p&gt;A coding agent burns through tokens. The monthly bill from a frontier API provider for a single moderately active agent lands somewhere between fifty and a few hundred dollars, and the natural reaction is to check whether a one-time hardware purchase would be cheaper. The naive comparison — dollars per million tokens versus dollars amortized over five years — almost always concludes that local wins. The honest comparison rarely does, at least for coding workloads, at least as of mid-2026. The reason is a capability gap that doesn&amp;rsquo;t show up in any cost spreadsheet.&lt;/p&gt;</description></item></channel></rss>