The OpenClaw DC buying guide
A short, opinionated ladder for OpenClaw hosts and local LLM machines — organized by memory fit, software tolerance, and the reason to skip each option.
Choose a Mac when low friction, efficiency, and shared memory matter more than CUDA. Memory is not upgradeable later — buy the configuration, not just the machine name.
Small, quiet, and easy to leave on. A strong OpenClaw host when cloud models do the heavy reasoning and local models stay modest.
Skip the base-memory configuration if local inference is part of the plan.
The portable lane for smaller local assistants, development, and OpenClaw work that follows you between desk and travel.
Skip when maximum local model capacity per dollar matters most.
The cleanest route to larger local models without building a tower, tuning drivers, or splitting work across system RAM and VRAM.
Skip when CUDA tooling or maximum tokens per second is non-negotiable.
NVIDIA remains the conservative choice for CUDA-first AI tooling. Move up only when memory or speed changes your real workload — not because the model number is newer.
The low-cost CUDA baseline for 7B–14B models, embeddings, and learning the stack without a flagship build.
Skip if 20B+ models are the actual target.
A power-efficient 16GB choice for practical local assistants when you want a new card and a mature CUDA path.
Skip the 8GB variant; skip entirely if raw speed is the priority.
The faster 16GB lane for interactive work, image generation, and a workstation that also needs strong general GPU performance.
Skip if the price approaches a healthy 24GB option.
The value workhorse: mature CUDA support and enough memory for many practical 20B–35B quantized models.
Skip unknown history, weak cooling, or a system without PSU headroom.
Much faster than a 3090, but still the same 24GB fit class. Best for interactive inference and mixed creative workloads.
Skip if you are paying a large premium but need more capacity.
The top consumer NVIDIA lane when 32GB changes the model, quant, or context you can keep on the GPU.
Skip if your entire workload already fits comfortably in 24GB.
AMD can be compelling when VRAM per dollar matters and your exact runtime is proven. Treat backend support as part of the purchase, not a detail to solve later.
A large-memory consumer card for users comfortable validating ROCm, Vulkan, llama.cpp, or LM Studio on their exact OS.
Skip if you want the least surprising AI tooling path.
Single-card 32GB capacity for local inference and development, aimed at buyers willing to build around AMD's software path.
Skip if CUDA-only tools are central to your workflow.
This is not the casual upgrade lane. It is for one-card memory capacity, professional workloads, and buyers who can justify workstation-class spend.
The one-card NVIDIA route for large models, high-memory inference, and professional workflows that outgrow consumer GPUs.
Skip if a 32GB card, high-memory Mac, or burst cloud rental is enough.
The ladder prioritizes usable memory, runtime compatibility, system fit, and honest purchase risk. It does not rank products by launch price, gaming benchmarks, or affiliate payout.