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VOOZH | about |
Feb. 4, 2026 / Hardware Insights
Direct answer first: Qwen3 Coder Next 80B A3B is one of the most hardware-friendly 80B-class coding models released so far. Thanks to its MoE design with roughly 3B active parameters, a single high-VRAM GPU can run it at full 256k context, and even dual consumer GPUs can handle the 3-bit version comfortably. VRAM, not raw...
Oct. 9, 2025 / How to Run LLMs Localy
For anyone running LLMs locally, the goal is always more performance for less cost. We obsess over VRAM, memory bandwidth, and squeezing every last token per second out of our hardware. While prompt processing (TTFT) is often fast, the token generation that follows can be a bottleneck, especially on memory-bandwidth-limited systems. This one-token-at-a-time process, called...
Oct. 8, 2025 / How to Run LLMs Localy
If you’ve gotten your hands on an AMD Ryzen AI Max+ 395 (Strix Halo) system, you already know the raw hardware is impressive. That massive pool of unified LPDDR5x memory is a game-changer for running large models locally. But unlocking its full potential isn’t just plug-and-play. The key to getting the best possible performance lies...
Sep. 29, 2025 / How to Run LLMs Localy
Unlock significant speed gains for large language models on your own hardware without sacrificing quality. Here’s how it works and how to set it up in popular inference engines. Why Local LLMs Run Slow If you run large language models on your own hardware, you know the biggest challenge is inference speed. Getting high-quality models...
Sep. 18, 2025 / How to Run LLMs Localy
You’ve spent weeks picking out the parts for a powerful new computer. It has a top-tier CPU, plenty of fast storage, and maybe even a respectable graphics card. You download your first large language model (LLM), excited to run it locally, only to find the experience is agonizingly slow. The text trickles out one word...
Sep. 15, 2025 / How to Run LLMs Localy
For local LLM enthusiasts, the race for models with larger “context lengths” feels like the next frontier. While developers boast models that can “remember” entire novels, the practical reality for anyone running hardware at home is that a bigger context window directly translates to a massive hit on your system’s resources, especially your precious VRAM....
Sep. 5, 2025 / How to Run LLMs Localy
Running large language models locally requires smart resource management. Quantization is the key technique that makes this possible by reducing memory requirements and improving inference speed. This practical guide focuses on what you need to know for local LLM deployment, not the mathematical theory[1] behind it. For the technical mathematical details of quantization, check out...