Is more VRAM always better?

Helpful for speed and large contexts on GPU paths. Some Lab sparse workloads are still dominated by system RAM and storage.

Can I start small and upgrade?

Yes. Begin with Home-class open models; expand storage and RAM before chasing frontier libraries.

Local AI Hardware Guide: RAM, GPU, and Storage for Home vs Lab

Hardware is the real rate limit

Local AI is not magic software alone. Model size, context length, and throughput map directly to RAM, VRAM, disk bandwidth, and cooling.

Home / everyday class

Modern multi-core CPU, 16–32 GB RAM minimum for light open models (more is better), SSD with tens of GB free, optional GPU. Good for private chat, drafting, and light code assist.

Pro workstation class

32–64 GB+ RAM, stronger GPU optional, fast NVMe for multiple models, quiet cooling for long sessions. Suits heavier code models and creative local pipelines.

Helmsman Lab class

128–256 GB+ RAM recommended for frontier sparse open libraries, 1 TB+ free fast NVMe for weights and working set, high-core CPU, optional multi-GPU. Jobs may take time — product honesty over fantasy tok/s.

What not to buy into

Phones and thin ultrabooks will not host frontier open libraries. Marketing that hides hardware floors is a red flag.

S◉LOCK approach

System requirements and tier paths (Home / Pro / Enterprise) are published so invitations match machines. We will not sell fantasy on underpowered boxes.

Next step: Explore the Helmsman model catalog, check hardware floors, or request Founding Preview access.

FAQ

Is more VRAM always better?: Helpful for speed and large contexts on GPU paths. Some Lab sparse workloads are still dominated by system RAM and storage.
Can I start small and upgrade?: Yes. Begin with Home-class open models; expand storage and RAM before chasing frontier libraries.

Local AI hardware guide (Home vs Lab)