<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom"><title>Ju Lin's AI Weblog: Pretraining</title><subtitle>An independent research notebook on AI engineering, agents, models and the systems around them.</subtitle><id>https://julin.ai/atom/tags/pretraining/index.xml</id><link rel="self" type="application/atom+xml" href="https://julin.ai/atom/tags/pretraining/index.xml"/><link rel="alternate" type="text/html" href="https://julin.ai/tags/pretraining/"/><author><name>Ju Lin</name></author><updated>2026-08-24T00:00:00+12:00</updated><entry><title>Marin 535B-A23B Starts Training, in the Open</title><id>https://julin.ai/2026/08/24/marin-open-training/</id><link rel="alternate" type="text/html" href="https://julin.ai/2026/08/24/marin-open-training/"/><published>2026-08-24T00:00:00+12:00</published><updated>2026-08-24T00:00:00+12:00</updated><category term="pretraining"/><category term="field-notes"/><category term="training"/><content type="html">&lt;blockquote&gt;
&lt;p&gt;🚢 Marin 535B-A23B started training this week! As usual, the whole process is open.&lt;/p&gt;
&lt;p&gt;Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow.&lt;/p&gt;
&lt;p&gt;Before kicking off the run, we trained a 4-rung scaling ladder from 1.6B-A61M (48B tokens) to 27.7B-A1.2B (926B tokens) to debug issues, and to make a forecast of our hero run. This is by far our biggest run, so definitely expecting the unexpected.&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;&lt;a href="https://x.com/percyliang/status/2090918065634684997"&gt;Percy Liang&lt;/a&gt;, announcing the run on X.&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;The Marin project is a great example of being open to AI. The whole process is public: the scaling ladder they ran to debug the pipeline before committing GPU-months to the real thing, the exact token counts, the exact FLOPs, even the admission that they&amp;rsquo;re &amp;ldquo;expecting the unexpected&amp;rdquo; on their biggest run yet.&lt;/p&gt;
&lt;p&gt;Most labs treat a run like this as a trade secret until the model ships. It&amp;rsquo;s great we see another public model build. The last public run at this scale was &lt;a href="https://huggingface.co/bigscience/bloom"&gt;BLOOM&lt;/a&gt;, BigScience&amp;rsquo;s 176B model. Marin&amp;rsquo;s 535B total parameters (23B active) puts it well past that, the biggest public pretraining run that I&amp;rsquo;m aware of.&lt;/p&gt;</content></entry><entry><title>Pretraining a Mini Kimi K3 for $252</title><id>https://julin.ai/2026/08/21/mini-k3/</id><link rel="alternate" type="text/html" href="https://julin.ai/2026/08/21/mini-k3/"/><published>2026-08-21T00:00:00+12:00</published><updated>2026-08-21T00:00:00+12:00</updated><category term="pretraining"/><category term="moe"/><category term="kimi"/><category term="field-notes"/><content type="html">&lt;p&gt;Vizuara AI Labs trained a miniature Kimi K3 from scratch: 1.02B parameters, 145M active, 5B tokens, one H200, $252.35.&lt;/p&gt;
&lt;p&gt;Not simplifying the architecture like &lt;a href="https://karpathy.github.io/2026/02/12/microgpt/"&gt;Karpathy&amp;rsquo;s microgpt&lt;/a&gt;, they kept &lt;a href="https://books.vizuara.ai/book/pretraining-a-mini-k3"&gt;Kimi K3&amp;rsquo;s MoE and attention design&lt;/a&gt; intact.&lt;/p&gt;
&lt;p&gt;That&amp;rsquo;s a surprisingly cheap way to learn pretraining (in real-world). They worked through expert collapse, data-mixing bugs, distributed-training bugs, kernels, and GPU utilization on a modern MoE architecture.&lt;/p&gt;
&lt;p&gt;A few things worth noting:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;5B tokens is probably too little for a 1B model. The authors agree the run was budget constrained. So the cheap cost might due to the training stopped early.&lt;/li&gt;
&lt;li&gt;Beating GPT-2 isn&amp;rsquo;t particularly meaningful when Mini K3 has roughly 10× the parameters.&lt;/li&gt;
&lt;li&gt;MoE at this scale is debatable. A smaller dense model trained on more tokens would likely be better if the goal was capability.&lt;/li&gt;
&lt;/ul&gt;</content></entry></feed>