Rongchai Wang
Jan 24, 2026 00:07
EigenAI achieves 100% reproducible LLM outputs on GPUs with beneath 2% overhead, enabling verifiable autonomous AI brokers for buying and selling and prediction markets.
EigenCloud has launched its EigenAI platform on mainnet, claiming to resolve a elementary downside plaguing autonomous AI programs: you possibly can’t confirm what you possibly can’t reproduce.
The technical achievement right here is critical. EigenAI delivers bit-exact deterministic inference on manufacturing GPUs—that means similar inputs produce similar outputs throughout 10,000 take a look at runs—with simply 1.8% further latency. For anybody constructing AI brokers that deal with actual cash, this issues.
Why LLM Randomness Breaks Monetary Functions
Run the identical immediate by way of ChatGPT twice. Completely different solutions. That is not a bug—it is how floating-point math works on GPUs. Kernel scheduling, variable batching, and non-associative accumulation all introduce tiny variations that compound into totally different outputs.
For chatbots, no one notices. For an AI buying and selling agent executing along with your capital? For a prediction market oracle deciding who wins $200 million in bets? The inconsistency turns into a legal responsibility.
EigenCloud factors to Polymarket’s notorious “Did Zelenskyy put on a swimsuit?” market as a case examine. Over $200 million in quantity, accusations of arbitrary decision, and in the end human governance needed to step in. As markets scale, human adjudication would not. An AI choose turns into inevitable—however provided that that choose produces the identical verdict each time.
The Technical Stack
Attaining determinism on GPUs required controlling each layer. A100 and H100 chips produce totally different outcomes for similar operations as a result of architectural variations in rounding. EigenAI’s resolution: operators and verifiers should use similar GPU SKUs. Their assessments confirmed 100% match charge on same-architecture runs, 0% cross-architecture.
The crew changed normal cuBLAS kernels with customized implementations utilizing warp-synchronous reductions and stuck thread ordering. No floating-point atomics. They constructed on llama.cpp for its small, auditable codebase, disabling dynamic graph fusion and different optimizations that introduce variability.
Efficiency price lands at 95-98% of normal cuBLAS throughput. Cross-host assessments on unbiased H100 nodes produced similar SHA256 hashes. Stress assessments with background GPU workloads inducing scheduling jitter? Nonetheless similar.
Verification By means of Economics
EigenAI makes use of an optimistic verification mannequin borrowed from blockchain rollups. Operators publish encrypted outcomes to EigenDA, the mission’s information availability layer. Outcomes are accepted by default however will be challenged throughout a dispute window.
If challenged, verifiers re-execute inside trusted execution environments. As a result of execution is deterministic, verification turns into binary: do the bytes match? Mismatches set off slashing from bonded stake. The operator loses cash; challengers and verifiers receives a commission.
The financial design goals to make dishonest unfavourable anticipated worth as soon as problem chance crosses a sure threshold.
What Will get Constructed Now
The quick functions are easy: prediction market adjudicators whose verdicts will be reproduced and audited, buying and selling brokers the place each resolution is logged and challengeable, and analysis instruments the place outcomes will be peer-reviewed by way of re-execution somewhat than belief.
The broader development right here aligns with rising enterprise curiosity in deterministic AI for compliance-heavy sectors. Healthcare, finance, and authorized functions more and more demand the type of reproducibility that probabilistic programs cannot assure.
Whether or not EigenAI’s 2% overhead proves acceptable for high-frequency functions stays to be seen. However for autonomous brokers managing vital capital, the power to show execution integrity could also be well worth the efficiency tax.
The total whitepaper particulars formal safety evaluation, kernel design specs, and slashing mechanics for these constructing on the infrastructure.
Picture supply: Shutterstock
