Hardware Insights
-
Aug. 17, 2026 / Featured
We Tested Qwen3.8 27B: How Much GPU and VRAM Do You Really Need?
Qwen3.8 27B is another model in the 27B class that looks particularly interesting for local inference. We wanted to find out what it actually takes to run it on consumer hardware, especially at longer context lengths. This article is based on our own llama.cpp benchmark results. We are not evaluating model intelligence, coding quality, or...
-
Aug. 12, 2026 / Hardware Insights
Muse Glimmer 30B Hardware Requirements and Benchmark Results
Meta’s Muse Glimmer 30B has been getting attention in local inference circles, and we wanted to know what it actually takes to run it on your own hardware. This article is not a review of the model’s reasoning or coding quality. We are strictly looking at hardware behavior. VRAM usage, context scaling, prompt processing speed,...
-
Aug. 7, 2026 / Hardware Insights
Is the 48 GB Mac Mini M4 Pro Good for Local LLMs?
If you want to stay in the Apple ecosystem and need a compact system for running local large language models, the Mac Mini with the M4 Pro and 48 GB of unified memory is currently the lowest configuration that makes practical sense. Apple also offers 24 GB versions of the Mac Mini, but they are...
-
Apr. 16, 2026 / Hardware Insights
What hardware you need for MiniMax-M2.7 230B (A10B) in 4-bit
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
What GPU for Running OpenClaw Locally
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
Best LLM for MacBook Pro with M5 Max and 32GB
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 / Hardware Insights
What Hardware for Gemma 4 26B and 31B LLM Local Use
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
Best Laptop for Running OpenClaw AI Agent Locally
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
Best Mini Computer (PC/Mac) for Running OpenClaw AI Agent
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...
