Qwen3.8-27B on One RTX 3090 vs Two: +20% Decode, +14% Cold Prefill, and 3x on Ca

<h1> Complete Guide to llm-d CNCF Sandbox — Kubernetes-Native Distributed LLM Inference Framework </h1> <p>At KubeCon Europe 2026 in Amsterdam, IBM Research, Red Hat, and Google

<!-- 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

<blockquote> <p><em>Originally published at <a href="https://llmkube.com/blog/turboquant-m5-max-quality-and-asymmetric" rel="noopener

<!-- SC_OFF --><div class="md"><p>Hey everyone, I've been working on getting Multi-Token Prediction (MTP) working with quantized GGUFs for Qwen3-27B and the results are pretty

<p>GPT-5.4 shipped on March 5. Seven weeks later, on April 23, 2026, OpenAI released GPT-5.5 — and the pace alone tells you something about where this race is headed. This isn't

<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

<!-- SC_OFF --><div class="md"><p>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks