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VOOZH | about |
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VOOZH | about |
Allan Witt is the co-founder and Editor-in-Chief of Hardware-Corner.net. Computers and the web have fascinated him since childhood. In 2011, he began training as an IT specialist at a mid-sized company while launching a tech blog on the side—quickly discovering a passion for writing about hardware and technology.
After completing his training, Allan worked as a system administrator for two years. Alongside that, he started building and upgrading custom gaming PCs at a local hardware shop. What began as a part-time project grew into a full-time career. Today, his work also focuses on building and optimizing PC systems for local AI and LLM workloads, combining hands-on experience with a passion for making complex tech easy to understand.
Apr. 16, 2026 / Hardware Insights
Running MiniMax-M2.7 230B locally requires extreme VRAM, even with 4-bit quantization, and a dual high-end GPU setup is the practical baseline today. This article shows real VRAM usage and performance from a dual RTX Pro 6000 Blackwell system using MXFP4 quantization, with a focus on hardware limits and inference speed. Test setup and model details...
Apr. 7, 2026 / Hardware Insights
Running OpenClaw locally is not the same as running a simple chat model. Once you move into agentic workflows with tool calling, long system prompts, and multi-step reasoning, the hardware requirements shift in a very specific way. VRAM becomes the primary constraint, memory bandwidth defines responsiveness, and model size directly affects reliability. This article focuses...
Apr. 5, 2026 / Local Agents
OpenClaw is a personal, self-hosted AI assistant platform designed to run on your own hardware while connecting to the communication tools you already use. Instead of being just a chat interface, it functions as an agent system—capable of reasoning, executing tasks, and interacting with software and services across multiple steps. A typical OpenClaw setup includes...
Apr. 3, 2026 / Featured
The new Gemma 4 models from Google DeepMind have landed, and for local LLM users this is one of the more practical releases in a while. The lineup gives us two interesting mid-size targets: a 26B MoE model (A4B) and a 31B dense model. Both support up to 256K context, tool calling, and personal agent-style...
Apr. 2, 2026 / Hardware Insights
Running OpenClaw locally is very different from running a chat UI. If you have already read guides like Best Mini Computer for Running OpenClaw AI Agent and Understanding OpenClaw Hardware Requirements, you know the bottleneck is not just loading a model. It is sustaining long agent loops with tool calls, large context, and repeated prompt...
Mar. 31, 2026 / Hardware Insights
Understanding OpenClaw Hardware Requirements OpenClaw is not a typical chat interface. It is an agentic system that continuously executes tools, runs shell commands, sets cron jobs, and manages files. This changes the hardware profile significantly. The main constraint is not just model size, but consistency. Agentic workflows require models that can follow tool calls, maintain...
Mar. 26, 2026 / LLM Hardware News
Intel is entering the local LLM space more seriously with the Arc B70, a 32 GB VRAM GPU aimed directly at inference workloads. The card is expected to release on April 2, with preorders already appearing on Newegg around the $949 mark. For local LLM users, this is one of the first sub-$1000 options with...
Mar. 24, 2026 / Hardware Insights
If you bought an RTX Pro 6000 Blackwell expecting full Blackwell support for local LLM inference, you will not get FlashAttention-4. That kernel only runs on datacenter Blackwell GPUs like NVIDIA B200 and on NVIDIA H100. Even though the branding says “Blackwell”, the underlying hardware is different in a way that directly affects inference performance....
Mar. 19, 2026 / Hardware Insights
The NVIDIA DGX Station built around the GB300 Grace Blackwell Ultra is not just another workstation with a big GPU. It is closer to a single-node inference server designed around one idea: remove the boundary between VRAM and system RAM while keeping GPU compute in control. You get 252 GB of HBM3e at 7.1 TB/s...
Mar. 4, 2026 / LLM Hardware News
Apple has officially introduced the M5 Pro and M5 Max. For most buyers this is another generational bump. For local LLM users, especially those running quantized 7B to 120B models on unified memory, this release is about two things: memory bandwidth and prompt processing. Apple is claiming up to 4x faster LLM prompt processing compared...
Feb. 26, 2026 / Hardware Insights
If you are running quantized LLMs locally, especially 4-bit models, memory bandwidth usually matters more than raw CUDA core count. Once the model fits in VRAM, inference speed is largely determined by how fast the GPU can stream weights from VRAM into the tensor cores. For 7B models this is less obvious. For 34B, 70B,...
Feb. 26, 2026 / Hardware Insights
Qwen3.5 27B fits comfortably on a 24 GB GPU up to 131k context in 4-bit, but becomes memory heavy at 262k. Qwen3.5 35B MoE in 4-bit is the more practical long-context model for 24 GB cards, and it is significantly faster in token generation despite having more total parameters. VRAM is still the main constraint,...
Feb. 17, 2026 / LLM Hardware News
If you run quantized LLMs locally, VRAM is your main constraint. 16GB is the practical entry point for 13B class models in 4-bit, and anything above 24GB opens the door to 70B with multi GPU setups. Between November 2025 and February 2026, pricing for 16GB and higher GPUs has moved sharply upward. This article focuses...
Feb. 16, 2026 / LLM Hardware News
The OpenClaw ecosystem just split into two new directions. A Go rewrite called PicoClaw and a Rust implementation called ZeroClaw both claim to run on $10 class hardware, including Raspberry Pi type boards. The Mac mini is no longer part of the story. For local LLM enthusiasts who followed the recent OpenClaw security controversy, this...
Feb. 15, 2026 / LLM Hardware News
A recent pull request to llama.cpp is delivering a measurable performance jump for recently released Qwen3 Coder Next, with tests showing a significant increase in both prompt processing and next token generation speeds. The largest gains are in token generation, which directly impacts real time coding and chat workflows. The changes come from a compute...
Feb. 6, 2026 / LLM Hardware News
Microsoft has joined Google and Amazon in the custom AI silicon race with Maia 200, its second-generation in-house accelerator focused on large language model inference. Following the earlier Maia 100, this iteration shows a clearer commitment to custom silicon as inference costs begin to dominate real-world AI deployments. Alongside Google’s TPU v7 and Amazon Trainium,...
Feb. 3, 2026 / LLM Hardware News
The short answer is yes. The longer answer is that Intel Xeon 600 makes sense for local LLM inference in very specific scenarios, mostly where memory bandwidth and system RAM capacity matter more than raw GPU compute. For local LLM users, especially those running large quantized models like 70B, 120B, or even bigger, the CPU...
Feb. 3, 2026 / LLM Hardware News
February is shaping up to be an interesting month for people who run LLMs locally. New versions of three of the most widely used open model families are expected to land soon: DeepSeek V4, Qwen 3.5, and GLM 5. These models sit at the center of the local LLM community, especially for users who care...
Jan. 26, 2026 / LLM Hardware News
In a recent interview with Club386, Intel Fellow Tom Petersen said clearly that Intel has no plans to build a direct competitor to AMD’s Ryzen AI Max+ platform, better known as Strix Halo. His comments suggest that within the current Panther Lake generation, Intel will not ship a large “big APU” with an oversized iGPU...
Jan. 26, 2026 / LLM Hardware News
This article tracks GPUs that make sense for local LLM inference in early 2026. We generally monitor 10GB and higher VRAM models because anything below that quickly becomes limiting for real workloads. For this specific deal roundup, only 16GB VRAM and higher GPUs are included, since they represent the practical floor for running modern quantized...