OmniJev/Flide: 🔥🔥 The extremely fast inference engine for decision model · GitHub

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Flide is fast inference infrastructure for Jev-style decision models. With exact state forking it is up to 9.5 times faster than vLLM and SGLang, and every answer is the one the model gives the question alone. It serves the Jev /v1/systemone API.

See the docs for every model, images, the API and all options.

uv venv -p 3.12 && source .venv/bin/activate
uv pip install flide torchvision
flide serve --model OmniJev/OneJev1.6-0.8B

Any model in the model table starts the same way, for example Perplexity’s decision model.

flide serve --model perplexity-ai/pplx-decider-flash-v1.2

Every one of them answers this request.

curl http://localhost:8000/v1/systemone -H "Content-Type: application/json" -d '{
  "state": "Order #1182 arrived with a cracked screen. The customer asks for a refund and attached two photos.",
  "questions": {
    "action": {"type": "choice", "instructions": "What should support do next?",
               "criteria": {"refund": "refund the order", "replace": "send a replacement", "escalate": "ask a human"}},
    "urgent": {"type": "noul", "instructions": "Does this need a reply within the hour?"}
  }
}'

The last three columns say how much faster Flide answers one request than the engine its release ships, as the median latency over the requests of each workload on one H200, where DI is the Decision Index. The chat models run against the faster of vLLM and SGLang, and OneJev against qev, the engine it used before Flide. KnowLine 35B runs with --quantize fp8, as its release serves it in FP8. A value marked ~ is the average over the sizes tested.

Apache-2.0. flide/kernels/gdn.py holds a kernel derived from two kernels of
flash-linear-attention (MIT); THIRD_PARTY_NOTICES lists it with
the other code Flide builds on.

If you use Flide in your research, please cite our paper.

@article{li2026flide,
  title   = {Flide: Accelerating Decision Inference with Exact State Forking},
  author  = {Li, Bobo and Song, Jiajia and Fei, Hao and Hsu, Wynne and Lee, Mong Li},
  journal = {arXiv preprint arXiv:2610.42148},
  year    = {2026}
}