{
  "schema": "aiinfra_vla_model_hardware_tracker_v1",
  "generated_at": "2026-09-14",
  "last_verified": "2026-09-14",
  "scope": "繁中 VLA 模型、框架與機器人運算硬體的官方證據及明確標記來源的公開部署參考快照，不是跨平台效能排名。",
  "method": [
    "優先採官方產品頁、開發文件或官方發布說明；公開開發者教學只在來源、條件與限制可逐項標示時收錄。",
    "把模型可用性、官方平台定位、硬體參考值和獨立實測分開。",
    "沒有公開固定硬體門檻就記錄缺口，不用其他模型的數字代替。",
    "供應商 benchmark 不視為本站實測，也不視為安全控制證明。",
    "開發者教學 benchmark 必須保留裝置、軟體、模型設定、功耗模式與工作負載；它只可作為可重查的參考，不可外推成通用效能。"
  ],
  "records": [
    {
      "id": "smolvla",
      "kind": "model",
      "name": "SmolVLA",
      "version_or_status": "LeRobot 開放模型",
      "stage": "推論／輕量工作負載",
      "hardware_signal": "Hugging Face 硬體指南把 SmolVLA 列在 24 GB VRAM 級的輕量工作負載，並提醒 VLA batch 1 仍可能吃緊。",
      "evidence_level": "官方文件",
      "not_a_conclusion": "不代表所有資料集、相機數、精度和微調設定都只需要 24 GB。",
      "source_urls": [
        "https://huggingface.co/docs/lerobot/main/en/hardware_guide",
        "https://huggingface.co/docs/lerobot/smolvla"
      ],
      "last_verified": "2026-08-16"
    },
    {
      "id": "molmoact2",
      "kind": "model",
      "name": "MolmoAct2",
      "version_or_status": "LeRobot v0.6.0 整合",
      "stage": "推論／LoRA 微調",
      "hardware_signal": "Hugging Face 官方發布說明列出約 12 GB bf16 推論、24 GB 單卡 LoRA 微調的參考值。",
      "evidence_level": "官方發布說明",
      "not_a_conclusion": "參考值不是本站實測，也不等同量產機器人的延遲或成功率。",
      "source_urls": [
        "https://huggingface.co/blog/lerobot-release-v060"
      ],
      "last_verified": "2026-08-16"
    },
    {
      "id": "lingbot-va",
      "kind": "model",
      "name": "LingBot-VA",
      "version_or_status": "LeRobot v0.6.0 模型",
      "stage": "推論",
      "hardware_signal": "Hugging Face 官方發布說明寫明推論可在單張 24-32 GB GPU 上執行。",
      "evidence_level": "官方發布說明",
      "not_a_conclusion": "不代表資料讀取、相機前處理、控制迴路和整機功耗已被涵蓋。",
      "source_urls": [
        "https://huggingface.co/blog/lerobot-release-v060"
      ],
      "last_verified": "2026-08-16"
    },
    {
      "id": "isaac-groot-n17",
      "kind": "model",
      "name": "Isaac GR00T N1.7",
      "version_or_status": "NVIDIA early access；LeRobot v0.6.0 已更新整合",
      "stage": "訓練／推論／部署研究",
      "hardware_signal": "NVIDIA 將 Jetson AGX Thor 放在 GR00T 的部署與即時機器人推論／控制脈絡；N1.7 公開頁面沒有給出通用 VRAM 門檻。",
      "evidence_level": "官方產品頁／發布說明",
      "not_a_conclusion": "early access 不等於商用支援，也不能把 Thor 的平台規格當成每個 GR00T 工作負載的保證。",
      "source_urls": [
        "https://developer.nvidia.com/isaac/gr00t",
        "https://huggingface.co/blog/lerobot-release-v060"
      ],
      "last_verified": "2026-08-16"
    },
    {
      "id": "gemini-robotics-2",
      "kind": "model",
      "name": "Gemini Robotics 2",
      "version_or_status": "Private preview／early-access waitlist",
      "stage": "跨機型 VLA／全身控制研究",
      "hardware_signal": "Google DeepMind 將它定位為可跨桌上型機器人到人形機器人的 VLA，輸入影像與文字、輸出動作；公開頁未列可自行部署的 GPU、記憶體或 runtime 規格。",
      "evidence_level": "官方模型頁",
      "not_a_conclusion": "展示、公司 benchmark 與跨機型定位不等於公開權重、一般開發者可部署、免調整或現場安全通過。",
      "source_urls": [
        "https://deepmind.google/models/gemini-robotics/vla/",
        "https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/"
      ],
      "last_verified": "2026-09-14"
    },
    {
      "id": "gemini-robotics-on-device-2",
      "kind": "model",
      "name": "Gemini Robotics On-Device 2",
      "version_or_status": "Trusted testers／early-access waitlist",
      "stage": "裝置端推論／新機型調整研究",
      "hardware_signal": "Google DeepMind 定位為本機執行的 VLA，稱新雙臂機型通常可用少於 200 個示例、數小時資料調整；公開頁未列通用部署硬體、模型大小、延遲或記憶體門檻。",
      "evidence_level": "官方模型頁／model card",
      "not_a_conclusion": "少於 200 個示例是供應商條件式結果；不保證所有任務、分布外情境、高自由度、移動或全身控制，也不是安全認證。",
      "source_urls": [
        "https://deepmind.google/models/gemini-robotics/on-device/",
        "https://deepmind.google/models/model-cards/gemini-robotics-on-device-2/"
      ],
      "last_verified": "2026-09-14"
    },
    {
      "id": "jetson-agx-thor",
      "kind": "platform",
      "name": "Jetson AGX Thor",
      "version_or_status": "機器人運算平台",
      "stage": "裝置端推論／機器人部署",
      "hardware_signal": "NVIDIA 官方資料列出 128 GB 記憶體、40-130 W 功耗範圍，並提供 GR00T N1／N1.5 的供應商 benchmark。",
      "evidence_level": "官方產品頁／供應商 benchmark",
      "not_a_conclusion": "不是所有 VLA 的獨立效能排名，也不是安全控制證明。",
      "source_urls": [
        "https://developer.nvidia.com/blog/introducing-nvidia-jetson-thor-the-ultimate-platform-for-physical-ai/",
        "https://developer.nvidia.com/isaac/gr00t"
      ],
      "last_verified": "2026-08-16"
    },
    {
      "id": "openpi-pi05-jetson-thor-reference",
      "kind": "deployment_reference",
      "name": "OpenPi π₀.₅ on Jetson AGX Thor",
      "version_or_status": "Jetson AI Lab 公開部署教學；固定工作負載的參考結果",
      "stage": "裝置端推論／模型部署",
      "hardware_signal": "教學在 Jetson AGX Thor Developer Kit、JetPack 7.2、MAXN、pi05_libero、action horizon 10 下，列出 PyTorch BF16 約 132／128 ms、TensorRT FP8 約 54／53 ms、TensorRT FP8 + NVFP4 約 49／48 ms（總延遲／模型延遲）；教學固定 OpenPi commit 15a9616。",
      "evidence_level": "開發者教學 benchmark／官方 JetPack 發布頁",
      "not_a_conclusion": "這是特定 LIBERO 工作負載與功耗模式的已發布教學結果，不是本站獨立實測、真機端到端延遲、所有 OpenPi checkpoint 的相容性保證，也不能取代控制與安全驗證。",
      "source_urls": [
        "https://www.jetson-ai-lab.com/tutorials/openpi_on_thor/",
        "https://developer.nvidia.com/embedded/jetpack/downloads/archive-7.2",
        "https://github.com/Physical-Intelligence/openpi/issues"
      ],
      "last_verified": "2026-08-29"
    },
    {
      "id": "jetson-agx-orin",
      "kind": "platform",
      "name": "Jetson AGX Orin",
      "version_or_status": "機器人／Edge AI 平台",
      "stage": "裝置端推論／機器人部署",
      "hardware_signal": "NVIDIA 官方產品資料列出最高 275 TOPS；Thor 官方文章另提供特定條件下的 Orin 對照。",
      "evidence_level": "官方產品頁／供應商 benchmark",
      "not_a_conclusion": "TOPS 和單一對照表不能取代模型、I/O、最差延遲、功耗與機械環境驗證。",
      "source_urls": [
        "https://www.nvidia.com/en-au/autonomous-machines/embedded-systems/jetson-orin/",
        "https://developer.nvidia.com/blog/introducing-nvidia-jetson-thor-the-ultimate-platform-for-physical-ai/"
      ],
      "last_verified": "2026-08-16"
    }
  ]
}