  Generative AI models, such as GPT-4 and Stable Diffusion, have demonstrated
powerful and disruptive capabilities in natural language and image tasks.
However, deploying these models in decentralized environments remains
challenging. Unlike traditional centralized deployment, systematically
guaranteeing the integrity of AI model services in fully decentralized
environments, particularly on trustless blockchains, is both crucial and
difficult. In this paper, we present a new inference paradigm called
\emph{proof of quality} (PoQ) to enable the deployment of arbitrarily large
generative models on blockchain architecture. Unlike traditional approaches
based on validating inference procedures, such as ZKML or OPML, our PoQ
paradigm focuses on the outcome quality of model inference. Using lightweight
BERT-based cross-encoders as our underlying quality evaluation model, we design
and implement PQML, the first practical protocol for real-world NLP generative
model inference on blockchains, tailored for popular open-source models such as
Llama 3 and Mixtral. Our analysis demonstrates that our protocol is robust
against adversarial but rational participants in ecosystems, where lazy or
dishonest behavior results in fewer benefits compared to well-behaving
participants. The computational overhead of validating the quality evaluation
is minimal, allowing quality validators to complete the quality check within a
second, even using only a CPU. Preliminary simulation results show that PoQ
consensus is generated in milliseconds, 1,000 times faster than any existing
scheme.
