<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tensorwire on Dustin Morgan</title><link>https://dustinmorgan.org/tags/tensorwire/</link><description>Recent content in Tensorwire on Dustin Morgan</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 28 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dustinmorgan.org/tags/tensorwire/index.xml" rel="self" type="application/rss+xml"/><item><title>Coming Soon: CIP and CTP</title><link>https://dustinmorgan.org/blog/coming-soon-cip-and-ctp/</link><pubDate>Mon, 28 Sep 2026 00:00:00 +0000</pubDate><guid>https://dustinmorgan.org/blog/coming-soon-cip-and-ctp/</guid><description>Two things I&amp;rsquo;ve been building in private: running big open-weight models across the machines you already have, and training on them too.</description></item><item><title>SQ4 Is Live: Whitepaper + llama.cpp PR</title><link>https://dustinmorgan.org/blog/sq4-is-live-llama-cpp-pr-and-whitepaper/</link><pubDate>Wed, 13 May 2026 00:00:00 +0000</pubDate><guid>https://dustinmorgan.org/blog/sq4-is-live-llama-cpp-pr-and-whitepaper/</guid><description>We published the SQ4 whitepaper and opened a PR to bring percentile-calibrated 4-bit quantization to llama.cpp.</description></item><item><title>v1.5.2: Fused Inference, FP16, and a Tokenizer That OOM'd a 64GB Machine</title><link>https://dustinmorgan.org/blog/v152-fused-inference-fp16-tokenizer/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://dustinmorgan.org/blog/v152-fused-inference-fp16-tokenizer/</guid><description>Inference +28%. Training +54% at dim≥1024. The tokenizer was O(n²) and nobody noticed until we fed it 48MB.</description></item><item><title>Benchmarks: I Owe PyTorch a Fair Fight</title><link>https://dustinmorgan.org/blog/benchmarks-i-owe-pytorch-a-fair-fight/</link><pubDate>Mon, 27 Apr 2026 00:00:00 +0000</pubDate><guid>https://dustinmorgan.org/blog/benchmarks-i-owe-pytorch-a-fair-fight/</guid><description>The 1.54x number has an asterisk. I ran PyTorch with default settings. Here&amp;rsquo;s what I still owe.</description></item><item><title>Why I Wrote a GPU Training Engine in Go</title><link>https://dustinmorgan.org/blog/why-i-wrote-a-gpu-training-engine-in-go/</link><pubDate>Mon, 20 Apr 2026 00:00:00 +0000</pubDate><guid>https://dustinmorgan.org/blog/why-i-wrote-a-gpu-training-engine-in-go/</guid><description>I got tired of Python&amp;rsquo;s toolchain for fine-tuning. So I wrote my own — and accidentally built something faster.</description></item></channel></rss>