Search

It depends entirely on the task, and the split is sharper than most coverage admits. On bounded work, single-file generation, unit tests, boilerplate, and explaining code, current open-weight models on a good workstation are at rough parity with frontier cloud models. On long-horizon agentic work, multi-file refactors across a large repository, they lose clearly, and the failure modes are the unpleasant kind: silent no-ops and confidently reported work that never happened. If your reason for going local is privacy, air-gap requirements, or cost, it is viable today. If your reason is capability

Agentic workloads such as coding agents, tool-calling pipelines, and multi-step autonomous tasks are throughput- and latency-sensitive in a way single-chat use is not, because agents chain many model calls with large context. That favors the tower tier: the Dell Precision 7875’s discrete VRAM delivers the sustained time-to-first-token that keeps long agent chains responsive. For budget-conscious agentic experimentation, a GB10-class appliance runs the same stacks at lower speed. Our full sizing guidance is in RAM, GPU & Storage for Agentic AI (coming soon).

For chat, 8GB of system RAM runs a 3B model, and 16GB runs a 7 to 8B model acceptably on CPU alone. For useful speed, you want the model in VRAM or unified memory: 16GB handles the 7 to 14B class, 24GB reaches the 30B class at Q4, and 32GB or more is where agentic coding work stops being frustrating. Above that, capacity matters more than bandwidth, which is why 96 to 128GB unified-memory machines run models that no consumer graphics card can hold.