Maverick: Private and Verifiable LLM Inference Made Practical via Matrix-Vector Multiplication Delegation
Open-source large language models (LLMs) are increasingly competitive with closed-source models while offering transparency and the ability to run inference without exposing user inputs to a service provider. However, running large-scale models locally requires substantial computational resources. In practice, users may still resort to a third-party provider, giving rise to privacy and correctness concerns. Existing solutions that address these problems often impose substantial server overhead or introduce additional trust assumptions. In this paper, we present Maverick, a novel approach to private and verifiable LLM inference based on a protocol for delegating matrix-vector multiplicatio...
| Authors | Ben Merbaum, Mohammad Amin Raeisi, Wenhao Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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