展示 HN: Flide:将类似 Jev 的决策模型速度提升最多 9.5 倍
Show HN: Flide: Make Jev-like decision models up to 9.5x faster

原始链接: https://github.com/OmniJev/Flide

Flide 是面向 Jev 风格决策模型的高速推理基础设施。借助精确状态分叉,它在保留每个模型独立答案的同时,最多可比 vLLM 和 SGLang 快 9.5 倍。它提供 `/v1/systemone` API,支持多种决策模型家族以及聊天模型,可处理文本、图像和视频输入。近期版本新增了大量模型家族、验证工具以及会话缓存、批次不变性、FP8 量化和多种前端等选项。基准测试在不同工作负载下将 Flide 与其他引擎进行比较,加速比因模型和任务而异。软件采用 Apache-2.0 许可证,其中一个内核源自 flash-linear-attention(MIT)。研究者请引用 Flide 论文。快速开始使用 `uv` 安装 Flide,运行 `flide serve` 加载模型,然后查询 localhost。文档涵盖模型、图像、API 及所有选项。

Flide 是一种新的高性能、面向 Jev-like 决策模型的精确推理引擎,宣称相比 vLLM 和 SGLang 最高可提速 9.5 倍。其主要优化是在对同一输入提出多个问题时先计算一次共享上下文,然后并行分支问题;它还使用预录制的 CUDA 图。加速在每个上下文问题很多时最大,而单问题情况从接近持平到约 3 倍更快。它支持 30+ 本地模型,可在 GPU 自托管,并可在 RTX 3050 等中低端 GPU 上运行小型模型。用户通过先放置共享状态并一次性提出所有问题获益最大,还可选会话缓存用于 agent 决策。限制包括当一个提问覆盖多个输入时获益更少,以及 GLiDE 等双向注意力模型尚不支持。
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原文

Flide: Extremely Fast Decision Inference with Exact State Forking

English · 简体中文 · 日本語

arXiv PyPI Website Docs License Python PyTorch

News · Quick start · Models · Options

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.

9.5 times faster than vLLM and SGLang; exact state forking; extremely fast

One state forked into three questions, each answered in one forward pass

Earlier news
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?"}
  }
}'

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

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.

Model Inputs DI MultiRC QuALITY
Ainvest-Decision text 1.4× 1.3× 1.3×
AJev text 3.7× 9.3× 2.6×
AutoTrust text, images ~3.2× ~7.6× ~1.6×
Bespoke-Nimble text 2.6× 2.1× 1.6×
cerno text ~4.1× ~8.6× 1.1×
Clef text, images 1.3× 1.0× 1.0×
Darwin text ~1.4× ~4.2× ~1.2×
Drex text, images ~2.8× ~6.2× ~1.5×
Gemma 4 text, images ~1.3× ~1.7× ~1.0×
Intern-Decision text, images 4.4× 1.1× 1.5×
JADE text 2.0× 9.2× 1.7×
Jeffan text 3.1× 2.6× 1.9×
JevAny text, images 1.5× 5.8× 1.2×
jiwo text ~1.9× ~5.9× 1.8×
Kev text ~7.9× ~1.5× ~1.7×
KnowLine text, images ~1.4× ~4.3× ~1.5×
Linne text 2.3× 8.6× 1.2×
Lumma text, images 1.7× 7.1× 1.2×
Lychee text 1.4× 1.5× 1.4×
mammoth text, images ~4.3× ~2.8× ~1.8×
mogan text 1.6× 8.5× 1.8×
OneJev text, images, video ~6.8× 7.3× 4.7×
Open-Jev text 4.2× 5.0× ~1.5×
pplx-decider text, images ~2.2× ~1.4× ~1.3×
Quyet text ~3.9× ~7.1× ~1.6×
Qwen3.5 text, images ~2.1× ~4.8× ~1.3×
Senova text 3.1× 28.1× 1.2×
Sieve text 3.0× 14.3× 2.6×
StartLux text 2.6× 1.1× 1.2×
SynACK Decide text 3.3× 11.9× 3.0×
torchcast text, images 1.6× 8.4× 1.4×
Wald text 1.6× 12.7× 1.5×
Xor text, images, video 2.5× 6.2× 2.8×
Option What it does
--session-cache-gb 40 keeps the states of earlier requests, so an agent's next step reads only what is new
--batch-invariant the same bits for a request whatever it is batched with, at some cost in speed
--quantize fp8 Qwen3.5 in FP8 on Ada, Hopper and Blackwell GPUs
--frontends 2 HTTP, rendering and tokenizing in two processes of their own, for many clients

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}
}
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