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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 / Hardware Insights
The MacBook Pro M5 Max with 32GB unified memory sits in an interesting spot for local LLM inference. It is not a maxed out configuration, but it is the minimum tier where modern 25B to 32B class models start to feel usable for real work. This article focuses on what actually runs, what is worth...
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. 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...
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,...