What Is Context Length in LLMs and How It Impacts Your VRAM (and Speed)
Learn what context length in large language models (LLMs) is, how it impacts VRAM usage and speed, and practical ways to optimize performance on local GPUs.| Hardware Corner
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 […]| Hardware Corner
When choosing a local LLM, one of the first specifications to check is its context window. The context size determines how many tokens you can feed into the model at once, which directly affects practical use cases like long-form reasoning, document analysis, or multi-turn conversations. For hardware enthusiasts running quantized models on limited VRAM, knowing […]| Hardware Corner
Learn what context length in large language models (LLMs) is, how it impacts VRAM usage and speed, and practical ways to optimize performance on local GPUs.| Hardware Corner
Explore the list of Qwen model variations, their file formats (GGML, GGUF, GPTQ, and HF), and understand the hardware requirements for local inference.| www.hardware-corner.net
Let’s be honest: cloud LLMs are incredibly powerful and mostly free. GPT-5, Gemini Pro, Claude Sonnet 4 – you can use them for almost unlimited queries without hitting hard limits. I personally combine Gemini and ChatGPT when one hits a rate limit, and it works perfectly. So why would you want to run models locally? […]| Hardware Corner
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 […]| Hardware Corner
Large Language Models (LLMs) have rapidly emerged as powerful tools capable of understanding and generating human-like text, translating languages, writing different kinds of creative content, and answering questions in an informative way. You’ve likely interacted with them through services like ChatGPT, Claude, or Gemini. While these cloud-based services offer convenience, there’s a growing interest in […]| Hardware Corner
For local LLM enthusiasts, VRAM has always been the main constraint when choosing hardware. Now, a new option is becoming more accessible at a price point that’s hard to ignore. The Huawei Atlas 300I Duo, an AI inference card from China, is showing up on platforms like Alibaba for under $1500, offering an impressive 96 […]| Hardware Corner
The latest rumors around AMD’s upcoming RDNA5 flagship, codenamed AT0, suggest a 512-bit memory bus paired with GDDR7. For anyone running large quantized LLMs locally, this is the part of the leak worth paying attention to – not the shader counts or gaming benchmarks. If the leak is accurate, bandwidth and VRAM capacity could finally […]| Hardware Corner
NVIDIA’s Jet-Nemotron claims a 45x VRAM reduction for local LLMs. Here’s what that really means for speed, context length, and consumer GPUs.| Hardware Corner
Moore’s Law Is Dead has leaked new details on AMD’s upcoming Medusa Halo APU, the direct successor to Strix Halo. For enthusiasts focused on running large language models locally, this is an important development, as Medusa Halo addresses the biggest bottleneck of its predecessor: memory bandwidth. From Strix Halo to Medusa Halo Strix Halo (Ryzen […]| Hardware Corner
The stream of mini-PCs built around AMD’s Ryzen AI 300 “Strix Halo” platform continues, this time with a new model named the X+ RIVAL. While the market is quickly becoming crowded with similar…| Hardware Corner
In a significant development for the AI community, the Qwen team has announced the release of its most powerful open agentic code model to date, the Qwen3-Coder-480B-A35B-Instruct.| Hardware Corner
The SIXUNITED STHT1 Mini-ITX motherboard brings AMD’s Strix Halo APU and 128GB of LPDDR5X memory to DIY LLM builders| Hardware Corner
We are testing the RTX 3090 with Qwen’s QwQ 32B parameter model using LM Studio. This is a real-world benchmark in Windows 11.| Hardware Corner
With a starting price of $2,999 (though the Founders Edition is listed at $3,999), the DGX Spark aims to democratize access to high-memory compute for local AI inference.| Hardware Corner
Find out what is the best laptop to run llama, mistral or deepseek large language models locally.| Hardware Corner
The landscape for high-density, on-premise AI hardware is rapidly evolving, driven almost single-handedly by the arrival of AMD’s Ryzen AI 300 “Strix Halo” series. For the enthusiast dedicated to…| Hardware Corner
The GMK EVO-X2, which was recently showcased at AMD’s “ADVANCING AI” Summit, is designed to meet this need, packing impressive AI processing capabilities into a small form factor.| Hardware Corner
The arrival of AMD’s Ryzen AI MAX+ 395 “Strix Halo” APU has generated considerable interest among local LLM enthusiasts, promising a potent combination of CPU and integrated graphics performance with…| Hardware Corner
Bosman launches the M5 AI Mini-PC with 128GB RAM and Ryzen AI MAX+ for just $1699 — the most affordable Strix Halo system yet for running local LLMs| Hardware Corner
Zotac unveils plans for the Magnus EA mini-PC with AMD Strix Halo APU, aiming to bring powerful local LLM inference to compact, GPU-free systems.| Hardware Corner
Beelink has unveiled the GTR9 Pro AI Mini, a compact LLM-ready PC powered by the Ryzen AI MAX+ 395 APU with up to 128GB RAM and 110GB usable VRAM—designed for local LLM inference in a small form factor.| Hardware Corner
Chinese manufacturer FAVM has announced FX-EX9, a compact 2-liter Mini-PC powered by AMD’s Ryzen AI MAX+ 395 “Strix Halo” processor, potentially offering new options for enthusiasts running quantized…| Hardware Corner
AMD’s Ryzen AI MAX+ 395 (Strix Halo) brings a unique approach to local AI inference, offering a massive memory allocation advantage over traditional desktop GPUs like the RTX 3090, 4090…| Hardware Corner
GMKtec has officially priced its EVO-X2 SFF/Mini-PC at ~$2,000, positioning it as a potential option for AI enthusiasts looking to run large language models (LLMs) at home.| Hardware Corner
With 96GB of GDDR7 memory, 1.79 TB/s memory bandwidth, RTX PRO 6000 is the first single-card workstation GPU capable of fully loading an 8-bit quantized 70B model such as LLaMA 3.3.| Hardware Corner
While NVIDIA’s newly announced RTX Pro 6000 offers a straightforward 96GB VRAM solution, , a new wave of modified RTX 4090 from China – offering 48GB per card – has emerged as a potential alternative.| Hardware Corner
It is surprisingly straightforward to increase the VRAM of your Mac (Apple Silicone M1/M2/M3 chips) computer and use it to load large language models. Here’s the rundown of my experiments.| Hardware Corner
