Swift 1.5 on veloGB10, ~110 tok/s on 2× DGX Spark: xhigh beats base Flash-Next a

<!-- SC_OFF --><div class="md"><p><em>A deep dive into implementing TurboQuant, validating its claims, and understanding where theory meets real-world systems</em></p> <h1>1.

<!-- SC_OFF --><div class="md"><p>I tested two llama.cpp builds on the same <strong>Qwen3.6-27B-NVFP4</strong> model.</p> <p><code>llama-bench</code> reports the model label as

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX2SQ594" rel="noopener noreferrer">System Design Pocket Guide: Interviews</a> </li> <li> <strong>Also by

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xavy8t/hardly_any_benchmarks_for_intel_b65_so_i_got_4x/"> <img alt="Hardly any benchmarks for Intel B65, so

<!-- SC_OFF --><div class="md"><p>**If this post gets enough traction, I’ll go back and run the full V4-Pro (1.6T params), rerun all of these experiments on it, plus run the

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vl6ior/i_gave_deepseek_v4_flash_basic_vision_by_training/"> <img alt="I gave DeepSeek V4 Flash basic vision

<!-- SC_OFF --><div class="md"><p>So i recently updated LMstudio after a long pause and updated my llama.cpp runtimes too.. i was shocked.. i thought maybe something like

<p>This is the data appendix for <strong>Posts 1–4</strong>.<br /><br /> The narrative and takeaways live in the main posts. This page is pure measurement.</p> <p><strong>Eval