NASA Puts Google’s Gemma Large Language Model in Orbit
IEEE Spectrum AI Matthew S. Smith
NASA's Jet Propulsion Laboratory deployed Google's Gemma 3 language model aboard a satellite for the first in-orbit demonstration of a vision-language model analyzing satellite imagery, showing that researchers can now interact with spacecraft using natural language prompts instead of structured commands. The 4-bit Gemma 3 4B model achieved 88 percent accuracy on a benchmark of 7,960 images without being trained on that specific dataset, and ran on an Nvidia Jetson Orin AGX module consuming only 8GB of memory. The capability enables satellites to compress image data into text summaries, potentially reducing wildfire detection delays from 90 minutes to near-real-time and opening a new paradigm for spacecraft control via prompts rather than traditional software updates.
Why it matters
The viability of orbital data centers hosting the largest and most capable large language models (LLMs) remains hotly contested. But enormous deployments that require thousands of GPUs aren’t the only way LLMs might prove useful in space. NASA’s Jet Propulsion Laboratory recently sent Google’s Gemma 3 to space, achieving the first in-orbit demonstration of a vision-language model analyzing imagery from a satellite’s own sensor.The system, known as NAVI-Orbital, used Gemma 3 to analyze images captured by a YAM-9 satellite built by Loft Orbital. Juan M. Delfa, technical group lead at NASA, said that though the goal in this case was image analysis, the project’s success implies a fundamentally new way researchers on the ground can interact with spacecraft.“This is a major shift,” said Delfa. “Now, a scientist can write a prompt, upload it to the spacecraft, and that will be taken into account by the system. It’s different from previous paradigms, where researchers have to write very struc
