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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. 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...
Jan. 26, 2026 / Hardware Insights
If you are running OpenClaw with a cloud model like Claude Opus, you do not need powerful hardware. Any modern low power system with 8 GB of RAM and a 6th+ gen Intel CPU is enough. If you want to run ClawdBot fully local with reliable tool usage and large context windows, hardware requirements scale...
Dec. 19, 2025 / LLM Hardware News
Apple quietly unlocked something important for local LLM users in macOS 26.2: RDMA over Thunderbolt. Combined with the public release of Exo 1.0, this turns multiple Mac Studios into a low latency memory pooled system that behaves very differently from the usual multi node setups local users are used to. This is not about cloud...
Dec. 9, 2025 / Hardware Insights
Unified memory has become one of the most important features for anyone running local LLMs in 2025. Instead of splitting memory between CPU RAM and GPU VRAM, unified architectures pool it into one high-bandwidth space that both the CPU and GPU can access. This matters because LLM inference is memory-bound long before it becomes compute-bound....
Oct. 11, 2025 / Hardware Insights
For years, the formula for running large language models locally has been simple: get as much VRAM as you can afford. This usually meant building complex, power-hungry desktop rigs with multiple GPUs or hunting for deals on used server hardware. But a new class of hardware, powered by Apple Silicon and AMD’s “Strix Halo” APUs,...