VQV Signal
According to a post on X by @GladiaLab, the lab published a paper describing Perseus, a full-stack system for encrypted model inference, with public code and a Python API. The paper reports that encrypted decoding of Llama-3-8B ran 3.96 times faster than the previous closed-source state of the art. The post links the work to the lab's homomorphic-inference speedrun, and the provided snippet does not include further benchmark details.
According to a post on X by @GladiaLab, the lab published a paper describing Perseus, a full-stack system for encrypted model inference, with public code and a Python API. The paper reports that encrypted decoding of Llama-3-8B ran 3.96 times faster than the previous closed-source state of the art. The post links the work to the lab's homomorphic-inference speedrun, and the provided snippet does not include further benchmark details.
AI-assisted summary based on the listed source.
The paper behind the lab's homomorphic-inference speedrun describes a full-stack system with public code and a Python API. It reports encrypted Llama-3-8B decoding 3.96× faster than the previous closed-source state of the art. Why it matters: Faster encrypted inference could let people use AI on sensitive data...
If the reported speedup holds up, encrypted inference could make it more practical to use AI on sensitive data without exposing that data to the model host. Developers and privacy-focused teams could examine the public code and Python API to test the system themselves. Watch for independent evaluation of the benchmark and the linked arXiv paper.
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VQV surfaced this signal because it is recent, relevant to Open Source LLMs, connected to Scobleizer News Pulse (X).
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