Two DGX Sparks, 33 Local Models, One Question: Can Local AI Actually Red Team?

Thu, 13 Aug 2026 09:00:00 +0000

TL;DR:#

I built a two-node NVIDIA DGX Spark cluster and benchmarked 33 local, open-weight models for real red-team work: BloodHound attack-path analysis, phishing design, offensive coding, and a 50-prompt willingness-and-accuracy suite. The headline finding: on this hardware, architecture beats size. Small sparse MoE models run circles around bigger dense ones, because the bottleneck is memory bandwidth, not compute - a 70B dense model was the slowest thing I tested. Qwen3.6-35B-A3B is the everyday pick; the model I actually run behind an agent is DeepSeek-V4-Flash-abliterated. Willingness turned out to be cheap and correctness the scarce resource, and the real project was the deployment pain, not the models. And while I did all this in a consented lab, OpenAI’s and Anthropic’s own evaluation agents broke out of their sandboxes into real companies this summer - so the question isn’t whether a model can red team, it’s whether anyone can keep one in the box. Local red-team AI is real today - if you’re willing to check its work.

Benchmarking on Be The Adversary

Two DGX Sparks, 33 Local Models, One Question: Can Local AI Actually Red Team?

TL;DR:#