Tether's Medical AI Runs on Your Phone and Outperforms Models 16x Its Size

In brief

• Tether's 1.7 billion-parameter QVAC MedPsy outperformed Google's MedGemma-4B and beat MedGemma-27B on HealthBench Hard, an OpenAI benchmark testing realistic clinical conversations graded by 262 physicians.
• The 4 billion-parameter model generates responses in ~909 tokens versus ~2,953 for comparable systems—a 3.2x reduction that makes local hospital and mobile deployment practical.
• Models ship in quantized GGUF format (1.2 GB and 2.6 GB) and run entirely on consumer hardware without cloud infrastructure.

Tether, the stablecoin company best known for USDT, just released a medical AI model that fits in your pocket and may outperform rivals more than a dozen times its size. QVAC MedPsy launched today from Tether's AI Research Group as a new class of medical language models designed to run on smartphones, wearables, and edge devices—no cloud required.

The headline number: a tiny 1.7 billion-parameter model capable of beating Google's MedGemma-4B on medical benchmarks despite being less than half its size. On HealthBench Hard—OpenAI's benchmark that evaluates AI on realistic, multi-turn clinical conversations graded by 262 physicians—Tether says its 1.7 billion-parameter model outscores MedGemma-27B, a model nearly sixteen times larger.

Parameters are all the configurations and values that a model learns during trading. The more the parameters, the better the model should be, in theory.

Source: Tether

The test suite spans MedQA-USMLE, which measures clinical knowledge using US medical licensing exam-style questions scored as percentage accuracy, all the way to AfriMedQA, which tests performance specifically for underserved African healthcare contexts.

Tether CEO Paolo Ardoino credited the gains to efficiency rather than scale. "With QVAC MedPsy, our focus was improving efficiency at the model level, rather than scaling up size," he said in a statement. "Our 4 billion model exceeded results from models nearly seven times its size, while using up to three times fewer tokens per response."

That token efficiency is the other headline. The 4B model averages around 909 tokens per response versus 2,953 for comparable systems—a 3.2x reduction. Fewer tokens means lower compute cost, faster responses, and crucially, the ability to run locally without a cloud backend.