EdgeRunner AI runs assistant on gpt-oss as open-weight models join US military
When I first saw EdgeRunner AI’s October paper, I was surprised to see a tiny startup tackling something the Pentagon has been chewing on for years: how to run powerful AI without ever touching the internet. The military’s old tug-of-war between bleeding-edge compute and the need for totally offline, secure systems seems to be loosening a bit. Open-weight models - the publicly released, non-proprietary versions of large language models - are apparently slipping into defense labs.
In the test, EdgeRunner loaded a cache of classified-style documents into an open-source model and then asked it a handful of mission-critical questions, all while staying completely off-cloud. The authors claim the answers hit the performance bar the service expects, which, if true, could be a game-changer. It suggests the armed forces might start favoring publicly available models they can fine-tune themselves, rather than leaning solely on commercial, closed-source AI.
The specifics of that claim are coming up next.
EdgeRunner AI, which is developing a virtual personal assistant for the military that doesn't require a cloud connection, says it achieved sufficient performance with gpt-oss after feeding it a cache of military documents to modify its capabilities, according to a paper the company published in October. The US Army and the Air Force will begin testing the modified model this month, says Tyler Saltsman, EdgeRunner's CEO. Open models may be particularly valuable in situations that require an immediate response or when internet interference could be an issue.
That includes AI systems running on drones or satellites, says Kyle Miller, a research analyst at Georgetown University's Center for Security and Emerging Technology. Open source AI models offer the military "a degree of accessibility, control, customizability, and privacy that is simply not available with closed models," he says. Beyond direct deals with AI providers, the military also has access to about 125 open source models and about 25 closed options through an intermediary AI platform called Ask Sage, says Nicolas Chaillan, the company's founder and a former chief software officer for the US Air Force and Space Force.
Chaillan says there are serious drawbacks to using open source models, particularly for the US military.
So what does this mean for military AI adoption? OpenAI’s open-weight models now give the DoD a locally runnable option, and EdgeRunner AI’s recent paper shows a prototype assistant that can operate without a cloud link after being tuned on a cache of classified documents. Early tests - at least according to a handful of defense vendors - suggest OpenAI’s tools still lag behind some rivals in a few key capabilities.
Still, many contractors seem relieved that a big industry player finally offers an on-premise model, easing the old dependence on closed-system solutions. The EdgeRunner work hints that gpt-oss can hit usable performance, but the paper says little about latency, reliability in the field, or integration headaches. It’s unclear whether the modest gains reported will hold up across the full range of defense tasks.
As we watch the military weigh these models against existing options, the trade-off between openness, security and functional parity will probably shape the next round of purchases.
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Common Questions Answered
How does EdgeRunner AI's virtual assistant achieve offline operation using gpt-oss?
EdgeRunner AI fine‑tuned the open‑weight gpt-oss model on a cached collection of military documents, allowing the assistant to run locally without a cloud connection. This offline capability meets the Pentagon’s need for secure, disconnected computing in the field.
Which branches of the U.S. military are slated to test the modified gpt-oss model, and when will testing begin?
The U.S. Army and the U.S. Air Force will begin testing the EdgeRunner‑modified gpt-oss model later this month, according to CEO Tyler Saltsman. The trials aim to evaluate the assistant’s performance in real‑world, secure environments.
Why are open‑weight models considered valuable for military AI deployments?
Open‑weight models are publicly available and can be run on isolated hardware, eliminating reliance on external cloud services that may pose security risks. This makes them attractive for defense applications that require both cutting‑edge AI capabilities and strict data sovereignty.
What limitations have early defense‑vendor tests identified in OpenAI’s open‑weight tools compared to rival offerings?
Early testing indicates that OpenAI’s open‑weight tools still lag behind competing solutions in certain key capabilities, such as nuanced reasoning and domain‑specific accuracy. Despite these gaps, contractors are relieved to have a major industry player providing a locally runnable alternative.