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v0.40.8-rc0：Flint 262K 上下文，macOS 支持https://devotd.net/en/news/nvidia-gtc-berlin-local-ai-inference-acceleration/devotd.en2026-10-21T15:45:54ZNVIDIA GTC Berlin: Local AI devices, faster inferencehttps://devotd.net/ja/news/nvidia-gtc-berlin-local-ai-inference-acceleration/devotd.ja2026-10-21T15:45:54ZNVIDIA GTC Berlin：ローカルAI機器、推論の高速化https://devotd.net/ko/news/nvidia-gtc-berlin-local-ai-inference-acceleration/devotd.ko2026-10-21T15:45:54ZNVIDIA GTC Berlin: 로컬 AI 기기, 추론 가속https://devotd.net/zh-hans/news/nvidia-gtc-berlin-local-ai-inference-acceleration/devotd.zh-cn2026-10-21T15:45:54ZNVIDIA GTC Berlin：本地 AI 设备，推理加速https://devotd.net/en/news/nvidia-rfd4-proteina-reward-guided-protein-design/devotd.en2026-10-21T13:52:04ZRFD4-Proteina: Generation speed, reward-guided searchhttps://devotd.net/ja/news/nvidia-rfd4-proteina-reward-guided-protein-design/devotd.ja2026-10-21T13:52:04ZRFD4-Proteina：生成速度、報酬に基づく探索https://devotd.net/ko/news/nvidia-rfd4-proteina-reward-guided-protein-design/devotd.ko2026-10-21T13:52:04ZRFD4-Proteina: 생성 속도, 보상 기반 탐색https://devotd.net/zh-hans/news/nvidia-rfd4-proteina-reward-guided-protein-design/devotd.zh-cn2026-10-21T13:52:04ZRFD4-Proteina：生成速度，奖励引导搜索https://devotd.net/en/news/qwen-3-8-omni-flash-agentic-audio-visual/devotd.en2026-10-21T09:43:58ZQwen3.8-Omni-Flash: Long-form audio and video understanding, 1M-token contexthttps://devotd.net/ja/news/qwen-3-8-omni-flash-agentic-audio-visual/devotd.ja2026-10-21T09:43:58ZQwen3.8-Omni-Flash：長時間の音声・動画理解、1Mトークンのコンテキストhttps://devotd.net/ko/news/qwen-3-8-omni-flash-agentic-audio-visual/devotd.ko2026-10-21T09:43:58ZQwen3.8-Omni-Flash: 장편 오디오·비디오 이해, 1M 토큰 컨텍스트https://devotd.net/zh-hans/news/qwen-3-8-omni-flash-agentic-audio-visual/devotd.zh-cn2026-10-21T09:43:58ZQwen3.8-Omni-Flash：长音视频理解，1M token 上下文https://devotd.net/en/news/krafton-raon-1-mini-preview/devotd.en2026-10-21T08:35:39ZRaon-1 mini preview: 30B-A4B MoE, up to 2.18× inference throughputhttps://devotd.net/ja/news/krafton-raon-1-mini-preview/devotd.ja2026-10-21T08:35:39ZRaon-1 mini preview：30B-A4B MoE、最大2.18倍の推論スループットhttps://devotd.net/ko/news/krafton-raon-1-mini-preview/devotd.ko2026-10-21T08:35:39ZRaon-1 mini preview: 30B-A4B MoE, 최대 2.18배 추론 처리량https://devotd.net/zh-hans/news/krafton-raon-1-mini-preview/devotd.zh-cn2026-10-21T08:35:39ZRaon-1 mini preview：30B-A4B MoE，最高2.18倍推理吞吐量https://devotd.net/en/news/ollama-v0-40-7/devotd.en2026-10-21T05:24:27ZOllama v0.40.7: Responses API fixes, model metadata exposurehttps://devotd.net/ja/news/ollama-v0-40-7/devotd.ja2026-10-21T05:24:27ZOllama v0.40.7：Responses APIの修正、モデルメタデータの公開https://devotd.net/ko/news/ollama-v0-40-7/devotd.ko2026-10-21T05:24:27ZOllama v0.40.7: Responses API 보정, 모델 메타데이터 노출https://devotd.net/zh-hans/news/ollama-v0-40-7/devotd.zh-cn2026-10-21T05:24:27ZOllama v0.40.7：Responses API 修正、模型元数据展示https://devotd.net/en/news/llama-cpp-b11774-lfm2-vision-layout/devotd.en2026-10-21T03:44:51Zllama.cpp b11774: Aligned LFM2 image processing, size-dependent token and timing changeshttps://devotd.net/ja/news/llama-cpp-b11774-lfm2-vision-layout/devotd.ja2026-10-21T03:44:51Zllama.cpp b11774：LFM2画像処理の整合、サイズ別のトークン数・時間の変化https://devotd.net/ko/news/llama-cpp-b11774-lfm2-vision-layout/devotd.ko2026-10-21T03:44:51Zllama.cpp b11774: LFM2 이미지 처리 정합, 크기별 토큰·시간 변화https://devotd.net/zh-hans/news/llama-cpp-b11774-lfm2-vision-layout/devotd.zh-cn2026-10-21T03:44:51Zllama.cpp b11774：LFM2 图像处理对齐，尺寸引起的令牌与耗时变化https://devotd.net/en/news/halogen-flash-server-0-18-12-npu-image-edit/devotd.en2026-10-21T02:33:50Zhalogen-flash-server 0.18.12: Strix Halo NPU inference acceleration, multi-image editinghttps://devotd.net/ja/news/halogen-flash-server-0-18-12-npu-image-edit/devotd.ja2026-10-21T02:33:50Zhalogen-flash-server 0.18.12：Strix HaloのNPU推論高速化、複数画像の編集https://devotd.net/ko/news/halogen-flash-server-0-18-12-npu-image-edit/devotd.ko2026-10-21T02:33:50Zhalogen-flash-server 0.18.12: Strix Halo NPU 추론 가속, 다중 이미지 편집https://devotd.net/zh-hans/news/halogen-flash-server-0-18-12-npu-image-edit/devotd.zh-cn2026-10-21T02:33:50Zhalogen-flash-server 0.18.12：Strix Halo NPU 推理加速，多图编辑https://devotd.net/en/news/deepspeed-0-19-8/devotd.en2026-10-21T02:06:45ZDeepSpeed v0.19.8: MoE optimization, parallelism expansionhttps://devotd.net/ja/news/deepspeed-0-19-8/devotd.ja2026-10-21T02:06:45ZDeepSpeed v0.19.8：MoEの最適化、並列化の拡張https://devotd.net/ko/news/deepspeed-0-19-8/devotd.ko2026-10-21T02:06:45ZDeepSpeed v0.19.8: MoE 최적화, 병렬화 확장https://devotd.net/zh-hans/news/deepspeed-0-19-8/devotd.zh-cn2026-10-21T02:06:45ZDeepSpeed v0.19.8：MoE 优化，并行化扩展https://devotd.net/en/news/nvidia-openshell-0-1-5/devotd.en2026-10-21T02:05:26ZNVIDIA OpenShell v0.1.5: Policy delivery, audit logginghttps://devotd.net/ja/news/nvidia-openshell-0-1-5/devotd.ja2026-10-21T02:05:26ZNVIDIA OpenShell v0.1.5：ポリシー配信、監査ログhttps://devotd.net/ko/news/nvidia-openshell-0-1-5/devotd.ko2026-10-21T02:05:26ZNVIDIA OpenShell v0.1.5: 정책 전달, 감사 로그https://devotd.net/zh-hans/news/nvidia-openshell-0-1-5/devotd.zh-cn2026-10-21T02:05:26ZNVIDIA OpenShell v0.1.5：策略推送，审计日志https://devotd.net/en/news/harness2-recursive-agent-harness/devotd.en2026-10-20T23:56:26ZHarness²: Automatic harness improvement, higher benchmark scoreshttps://devotd.net/ja/news/harness2-recursive-agent-harness/devotd.ja2026-10-20T23:56:26ZHarness²：ハーネスの自動改善、ベンチマーク性能の向上https://devotd.net/ko/news/harness2-recursive-agent-harness/devotd.ko2026-10-20T23:56:26ZHarness²: 하네스 자동 개선, 벤치마크 성능 상승https://devotd.net/zh-hans/news/harness2-recursive-agent-harness/devotd.zh-cn2026-10-20T23:56:26ZHarness²：自动改进智能体运行框架，提升基准性能https://devotd.net/en/news/deepseek-deepinfer-open-source-inference-framework/devotd.en2026-10-20T11:22:52ZDeepInfer: Offline inference, service componentshttps://devotd.net/ja/news/deepseek-deepinfer-open-source-inference-framework/devotd.ja2026-10-20T11:22:52ZDeepInfer：オフライン推論、サービス構成要素https://devotd.net/ko/news/deepseek-deepinfer-open-source-inference-framework/devotd.ko2026-10-20T11:22:52ZDeepInfer: 오프라인 추론, 서비스 구성요소https://devotd.net/zh-hans/news/deepseek-deepinfer-open-source-inference-framework/devotd.zh-cn2026-10-20T11:22:52ZDeepInfer：离线推理，服务组件https://devotd.net/en/news/ollama-0-40-7-rc0-responses-speculative-pplx/devotd.en2026-10-20T07:26:12ZOllama v0.40.7-rc0: Reasoning and draft handling improvements, decider model supporthttps://devotd.net/ja/news/ollama-0-40-7-rc0-responses-speculative-pplx/devotd.ja2026-10-20T07:26:12ZOllama v0.40.7-rc0：推論・ドラフト処理の改善、判定モデルのサポートhttps://devotd.net/ko/news/ollama-0-40-7-rc0-responses-speculative-pplx/devotd.ko2026-10-20T07:26:12ZOllama v0.40.7-rc0: 추론·초안 처리 개선, 판정 모델 지원https://devotd.net/zh-hans/news/ollama-0-40-7-rc0-responses-speculative-pplx/devotd.zh-cn2026-10-20T07:26:12ZOllama v0.40.7-rc0：改进推理与草稿处理，支持判定模型https://devotd.net/en/news/minimax-m31-flash/devotd.en2026-10-20T04:24:35ZMiniMax M3.1 Flash: agent benchmark results, claimed 70% task cost reductionhttps://devotd.net/ja/news/minimax-m31-flash/devotd.ja2026-10-20T04:24:35ZMiniMax M3.1 Flash：エージェント評価の成績、タスクコスト70%削減の主張https://devotd.net/ko/news/minimax-m31-flash/devotd.ko2026-10-20T04:24:35ZMiniMax M3.1 Flash: 에이전트 평가 성적, 작업 비용 70% 절감 주장https://devotd.net/zh-hans/news/minimax-m31-flash/devotd.zh-cn2026-10-20T04:24:35ZMiniMax M3.1 Flash：智能体评测成绩、任务成本降低70%的主张https://devotd.net/en/news/llama-cpp-iq-k-quantization-proposal/devotd.en2026-10-20T03:20:38Zllama.cpp PR #30715: IQ*_K Quantization, CPU Inferencehttps://devotd.net/ja/news/llama-cpp-iq-k-quantization-proposal/devotd.ja2026-10-20T03:20:38Zllama.cpp PR #30715：IQ*_K量子化、CPU推論https://devotd.net/ko/news/llama-cpp-iq-k-quantization-proposal/devotd.ko2026-10-20T03:20:38Zllama.cpp PR #30715: IQ*_K 양자화, CPU 추론https://devotd.net/zh-hans/news/llama-cpp-iq-k-quantization-proposal/devotd.zh-cn2026-10-20T03:20:38Zllama.cpp PR #30715：IQ*_K 量化，CPU 推理https://devotd.net/en/news/physical-plausibility-evaluator-screening/devotd.en2026-10-20T00:00:38ZNVIDIA Physical Plausibility Evaluator: Single-video screening, customizable decision criteriahttps://devotd.net/ja/news/physical-plausibility-evaluator-screening/devotd.ja2026-10-20T00:00:38ZNVIDIA Physical Plausibility Evaluator：単一動画の選別、独自の判定基準https://devotd.net/ko/news/physical-plausibility-evaluator-screening/devotd.ko2026-10-20T00:00:38ZNVIDIA Physical Plausibility Evaluator: 단일 영상 선별, 맞춤형 판정 기준https://devotd.net/zh-hans/news/physical-plausibility-evaluator-screening/devotd.zh-cn2026-10-20T00:00:38ZNVIDIA Physical Plausibility Evaluator：单视频筛选，定制判定标准https://devotd.net/en/news/apertus-1-5-early-fusion-multimodal-long-context/devotd.en2026-10-19T21:43:55ZApertus 1.5: expanded multimodal input, generational performance gains and evaluation disclosurehttps://devotd.net/ja/news/apertus-1-5-early-fusion-multimodal-long-context/devotd.ja2026-10-19T21:43:55ZApertus 1.5：マルチモーダル入力の拡張、前世代からの性能向上と評価の公開https://devotd.net/ko/news/apertus-1-5-early-fusion-multimodal-long-context/devotd.ko2026-10-19T21:43:55ZApertus 1.5: 멀티모달 입력 확장, 전세대 성능 개선과 평가 공개https://devotd.net/zh-hans/news/apertus-1-5-early-fusion-multimodal-long-context/devotd.zh-cn2026-10-19T21:43:55ZApertus 1.5：多模态输入扩展、较上一代的性能提升与评估公开https://devotd.net/en/news/gemini-4-argon-github-copilot/devotd.en2026-10-19T21:18:51ZGemini 4 Argon: General availability, per-token pricinghttps://devotd.net/ja/news/gemini-4-argon-github-copilot/devotd.ja2026-10-19T21:18:51ZGemini 4 Argon：正式提供、トークン別料金https://devotd.net/ko/news/gemini-4-argon-github-copilot/devotd.ko2026-10-19T21:18:51ZGemini 4 Argon: 정식 제공, 토큰별 요금https://devotd.net/zh-hans/news/gemini-4-argon-github-copilot/devotd.zh-cn2026-10-19T21:18:51ZGemini 4 Argon：正式推出，按 token 计费