vLLM语义路由器团队拥有发布 d3-ultra,Apache-2.0下的27B型多式联运决定模型。 d3-超决定模型不生成文本。 它读过一次输入，并返回每个问题答案的概率。 对于开发者来说，这意味着1个开放量级呼叫的路由，分类，视觉地面和视频检查。
TL；数据
- 尺寸：26.09B参数(强度，包括一个0.46B视觉编码器)；每个问题131,072个上下文符号。
- 运行在：1 AMD Instinct MI325X上测试，重量为BF16；尚未列出正式的量化变体。
- 性能：在球队自己的私人测试中，6个决定模型的最高SR-Text得分。
- 最佳：97.8关于SR-Text工具和自动化(chance校正精度)。
- 最糟的是：SR-Box上54.6%(9个域)，低于其兄弟d3涡轮的5.0分。
什么是d3 -ltra? -什么是D3 -ltra?
d3-ultra是一个来自vLLM语义路由器的System One决定模型，以概率解答打字的问题。 它是第3.0号决定的最新成员，这个家庭从0.6B到27B不等。
您发送一个状态( 文本或 JSON，加上图像或视频) 和一组问题。 每个问题都有一个类型：
- 选择：从几个选项中选一个。
- 是/否：还原为是的概率。
- 得分：比例。
找到影像或视频瞬间的东西
追踪整个视频的目标
图像也可以坐在问题和选项内部。 测验可以显示照片，然后提供4个蚀刻作为答案。
d3 -ltra是如何工作的？
核心思想是州一次。 模型读出每个请求的输入，并回答该单项的每个问题。
配置的显示 Qwen3.5-建筑骨干有64层。 48系线性视星等，16系全视星等。 该卡没有标明其基地检查站的名称。 VLLM的配方称兄弟姐妹d3是从Qwen3.8-27B训练出来的。
盒式答案总是满怀信心地返回一个盒子。 接近0的自信意味着目标不在画面中。 超过 131 072 个令牌的问题以 max 长度超过失败；没有任何东西是静默的。
d3-ltra在基准测试上表现如何？.
研究团队将d3-ultra与GLiDE V1.2,d1-ultra,d3-turbo,d3和Jev 1.14进行了2次内部测试。
SR-Text有10,411个文本请求，有73,495个问题。 大部分用新物品从34个公共基准测试重现技能。 另外4,896个问题来自43个新任务家庭，如SQL检查和售票路线。 得分是偶然的：0是偶然的，100是完美的。
- d3-极：87.2
- GLiDE V1.2: 85.1
- d1-超音速：83.4
- d3涡轮：83.1
- d3: 81.4
- 捷夫1.14:73.1
d3-ltra引导除艺术和人类品味以外的每个领域。 在那里，d1-ltra在误差幅度内得分90.8比89.6.
SR-Box有5,678个方框问题，涉及10个领域的4,984个图像。 在IoU 0.5或更高时，一个框算得正确。 d3-超音速得分58.2%,10个域的得分54.6%,9.d3-涡轮的得分60.0%,59.6%. Jev 1.14的得分45.7%,46.9%. 团队称 d3-涡轮在自然影像上保持较强，而d3-ultra则在图表上领先。
latency：在1AMD Instinct MI325X与AutoModel上，关于一张照片的1个问题需要304ms. 255个问题需要2.16 s，每个额外问题约7.3 ms.
如何部署d3 -ltra?
有4条路线：
- 变压器：从 prered("vllm-sr/d3-ultra"，信任 remote code=True)自动模具，带有变压器=5.17.0. 可选的闪光线-注意包新增快速GPU内核。
- vLLM语义路由器引擎：vllm-sr service -e vllm-sr/d3-ultra,2026年10月25日加入PR#5449.
- 免费API：卡片称 decision.vllm-sr.ai服务于d3-ultra，带有自由键。
- Demo：官方Hugging Face空间。
d3-ultra vs GLiDE V1.2, d1-ultra, Jev 1.14和d3-涡轮
关键外卖
- d3-ultra是一个27B Apache-2.0决定模型，从未生成文本。
- 它读过一次输入：255个问题，大约一张照片，取2.16秒。
——超过球队的SR-Text测试87.2，领先GLiDE V1.2 85.1.
- 它跟踪d3涡轮在Box地面：58.2%对60.0%。
- 所有基准测试数字在私人测试机上自报。
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The vLLM Semantic Router team has released d3-ultra, a 27B multimodal decision model under Apache-2.0. The d3-ultra decision model does not generate text. It reads an input once and returns a probability for every answer to every question. For developers, that means routing, classification, visual grounding and video checks from 1 open-weight call.
TL;DR
- Size: 26.09B parameters (dense, including a 0.46B vision encoder); 131,072 tokens of context per question.
- Runs on: tested on 1 AMD Instinct MI325X with BF16 weights; no official quantized variant is listed yet.
- Performance: highest SR-Text score of the 6 decision models in the team’s own private test.
- Best: 97.8 on SR-Text tools and automation (chance-corrected accuracy).
- Worst: 54.6% on SR-Box (9 domains), 5.0 points below its sibling d3-turbo.
What is d3-ultra?
d3-ultra is a System One decision model from vLLM Semantic Router that answers typed questions with probabilities. It is the newest member of Decision 3.0, a family that spans 0.6B to 27B.
You send a state (text or JSON, plus images or videos) and a set of questions. Each question has a type:
- Choice: pick 1 of several options.
- Yes / No: return the probability of yes.
- Score: rate on a scale.
- Box: locate something in an image or a video moment.
- Track: follow a target across a whole video.
Images can also sit inside questions and options. A quiz can show a photo, then offer 4 etchings as answers.
How does d3-ultra work?
The core idea is state once. The model reads the input once per request and answers every question from that single read.
The config shows a Qwen3.5-architecture backbone with 64 layers. 48 are linear-attention layers and 16 are full-attention layers. The card does not name its base checkpoint. vLLM’s recipe says the sibling d3 was trained from Qwen3.8-27B.
Box answers always return exactly 1 box with a confidence. A confidence near 0 means the target is not in the picture. Questions over 131,072 tokens fail with max_length_exceeded; nothing is silently truncated.
How does d3-ultra perform on benchmarks?
The research team compares d3-ultra with GLiDE V1.2, d1-ultra, d3-turbo, d3 and Jev 1.14 on 2 in-house tests.
SR-Text has 10,411 text requests with 73,495 questions. Most recreate skills from 34 public benchmarks with new items. Another 4,896 questions come from 43 new task families, like SQL checks and ticket routing. Scores are chance-corrected: 0 is chance, 100 is perfect.
- d3-ultra: 87.2
- GLiDE V1.2: 85.1
- d1-ultra: 83.4
- d3-turbo: 83.1
- d3: 81.4
- Jev 1.14: 73.1
d3-ultra leads every area except arts and human taste. There, d1-ultra scores 90.8 against 89.6, within the margin of error.
SR-Box has 5,678 Box questions on 4,984 images across 10 domains. A box counts as correct at IoU 0.5 or higher. d3-ultra scores 58.2% over 10 domains and 54.6% over 9. d3-turbo scores 60.0% and 59.6%. Jev 1.14 scores 45.7% and 46.9%. The team says d3-turbo stays stronger on natural images, while d3-ultra leads on charts.
Latency: on 1 AMD Instinct MI325X with AutoModel, 1 question about a photo takes 304 ms. 255 questions take 2.16 s, about 7.3 ms per extra question.
How do you deploy d3-ultra?
There are 4 routes:
- Transformers: AutoModel.from_pretrained("vllm-sr/d3-ultra", trust_remote_code=True) with transformers==5.17.0. The optional flash-linear-attention package adds fast GPU kernels.
- vLLM Semantic Router Engine: vllm-sr serve -e vllm-sr/d3-ultra, added in PR #5449 on October 25, 2026.
- Free API: the card says decision.vllm-sr.ai serves d3-ultra, with a free key.
- Demo: the official Hugging Face Space.
d3-ultra vs GLiDE V1.2, d1-ultra, Jev 1.14 and d3-turbo
Key Takeaways
- d3-ultra is a 27B Apache-2.0 decision model that never generates text.
- It reads input once: 255 questions about 1 photo take 2.16 s.
- It tops the team’s SR-Text test at 87.2, ahead of GLiDE V1.2 at 85.1.
- It trails d3-turbo on Box grounding: 58.2% vs 60.0%.
- All benchmark numbers are self-reported on private test sets.
Check out the model card, the GitHub repo, the Decision 3.0 collection and the Hugging Face Space. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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