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10月23日周五
10月22日周四
  1. IT之家·人工智能66

    讯飞星火 X2.6 端云一体大模型发布,首次基于全国产算力实现代码和智能体能力突破

    科大讯飞在全球 1024 开发者节发布讯飞星火 X2.6 端云一体大模型,宣称首次基于全国产算力实现代码和智能体能力突破。模型采用 293B-A30B 参数的 MoE 架构,上下文长度拓展至 1M,输入限时 0.9 元/百万 tokens,输出限时 3.6 元/百万 tokens;同时开源星火 X2.6-4B 端侧模型,新增三档推理模式,推理定价限时免费。

  2. OpenRouter62

    OpenRouter 宣布 Recursive 的首个模型 Flint 上架,支持免费试用。该模型为开源权重 40B MoE(约 2.5B 激活),由其自动化研究系统 Eureka Machine 构建,面向编码智能体,具备 262K 上下文、推理和工具调用能力。Recursive 报告的成绩为 Terminal-Bench 2.1 54.9%、SWE-bench Pro v2 89.7%、SWE-bench Verified 74.2%;试用地址 https://openrouter.ai/recursive/flint-v1.0:free。

    引用Richard Socher@RichardSocher

    Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

  3. MiniMax (official)56

    MiniMax 转发并致谢 SGLang 团队:SGLang-TorchTPU 由 SGLang 与 Google TPU 团队、Meta PyTorch 团队共建,可将 MiniMax H3、GLM-5.3 Flash、Kimi K3 等开源模型部署在 TPU 上,并已支撑 Meta 生产环境推理。Muse Spark Open 在 InferenceX 8K/1K、bs=1 下解码达 770 tok/s,项目已在 GitHub 开源。MiniMax 表示期待高效视频推理方向更多开源工作。

    引用SGLang@sgl_project

    Today we launch SGLang-TorchTPU, built with @Google TPU team and Meta @PyTorch team. We’re excited to see it powering production inference at Meta! SGLang-TorchTPU is a TPU plugin for SGLang that brings: - Top open models on TPU: Muse Spark Open, GLM-5.3 Flash, Kimi K3, Ling 3.1 Flash, MiniMax H3 - SGLang’s production features for large agentic workloads - Custom MegaKernel: Muse Spark Open hits 770 tok/s decode at bs=1 on InferenceX 8K/1K SGLang-TorchTPU is available now: http://github.com/sgl-project/sglang-torchtpu

  4. Rohan Paul64

    Recursive 发布首个模型 Flint,一个 40B MoE 本地编码模型,由其自动化研究系统 Eureka Machine 构建,从零开始用时不到 4 个月,权重以 Apache 2.0 开放。

    引用Richard Socher@RichardSocher

    Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

10月21日周三
  1. Rohan Paul43

    EAGLE-4 在 27B 模型单请求下达到 863.8 tokens/s,为 DFlash 2 的 1.28 倍、DSpark 的 1.40 倍。它保留“草稿—验证”循环,改进集中在草稿侧:自适应草稿结构和更廉价的草稿 LM head,由小模型预猜多个 token、目标模型一次前向校验,输出结果不变,测试环境约快 3 倍。

    引用Eagle@EagleCorp

    EAGLE-4 is here. From EAGLE-1 to EAGLE-3, we’ve kept making inference faster. EAGLE-4 is our fastest yet. 961.6 tokens/s on @Alibaba_Qwen’s Qwen3.8-27B with TokenSpeed (BF16, 4× @NVIDIA B300, TP4, one request, T=0). Github: https://github.com/eagle-hq/EAGLE-4

  2. Hao AI Lab54

    ComfyUI 宣布支持 Hao AI Lab 的 FastH3 OmniRef,这是 MiniMax H3 的蒸馏版本,可进行多模态参考视频生成,能在本地硬件上运行。官方称其在质量与速度之间取得较好平衡,并开放了权重与云端入口。

    引用ComfyUI@ComfyUI

    FastH3 OmniRef by @haoailab's is now supported in ComfyUI. This distilled version of Minimax H3 allows multi-modal reference generation of videos. It runs locally on your hardware and provide great balance between quality and speed. Click the links below for open weights and cloud ⬇️

  3. Fireworks AI76

    Meta 开放权重模型 Muse Spark Open 上线 Fireworks 美国区 serverless

    Meta 的开放权重模型 Muse Spark Open 在 Fireworks 上线 Day-0 serverless,为 816B 参数 MoE 架构,每 token 激活 42B 参数,支持 1M token 上下文窗口。

    推荐理由:Fireworks 给出了 Muse Spark Open 在法律、金融、医疗等多个独立基准的具体分数和上线方式,读者可据此评估其在受监管工作中的适用性。

  4. Rohan Paul54

    SoMark 团队发布开源文档解析模型 OXR(1.3B 参数),将 PDF 和文档图像转为含布局坐标的 Markdown 或 JSON 的 Agent-Friendly 数据。

    引用SoMark@somarkai

    Beyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench🏆 #1 in parsing speed among open-source models⚡ RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! 🌟