The roadmap is beautifully written: DeepProve will support LLaMA, Gemma, Claude, and Gemini, and will expand to new proof types such as training, fine-tuning, fairness, and reasoning links; this is good news for the industry, but there are three significant engineering hurdles.
The first is the cost curve. The proof circuit of Transformers is deep and memory-intensive; when doing 'complete inference proofs', the time and cost of proof generation may grow at a 'polynomial level'. The network can alleviate this with horizontal scaling and GPU/custom chips, but it needs to be clarified who will bear the extreme complexity: developers, end-users, or DAO subsidies? The roadmap has already pointed to 'GPUs, custom chips, cloud integration' to lower the cost of a single proof, and the speed of landing this part will directly determine whether 'mainstream models can run'.
The second is parallelism and scheduling. Large model inference can be chunked and verified in parallel, but the boundary consistency and aggregation costs brought by chunking can erode profits; this is the very meaning of 'turning proof into a network'—breaking down heavy tasks into routable, retriable, and replayable small units, constraining operator behavior onto the right track with reputation and deposits. Lagrange places this layer on EigenLayer, supported by 85+ operators, to make 'parallel + scheduling + restoration' a system default capability, rather than proprietary scripts of integrators.
The third is privacy and compliance. Enterprises will not expose private models and sensitive inputs directly on the chain; the 'confidential proofs' mentioned in the roadmap are key: proof is public, inputs remain confidential, allowing verification while protecting business secrets. This is also the prerequisite for zkML to truly enter 'healthcare/finance/government'.
In summary: supporting large models is not just a slogan, but a three-way balance of cost, parallelism, and privacy. Only by clarifying these three aspects can we turn 'roadmap popularity' into 'production-level capability'.
@Lagrange Official #lagrange $LA