DeepSeek V4 Flash at 8.4 tok/s on 3×3090: patching the GGUFs that won't load on

<!-- 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>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out

<!-- 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

<!-- 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

<!-- 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

<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>That foodtruck bench post showing deepseek v4 matching gpt-5.2 at 17x cheaper got me thinking. if frontier cloud models are that overpriced for

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