{
  "data": {
    "a": {
      "slug": "nemotron-3-nano-omni-30b-a3b-reasoning",
      "name": "Nemotron 3 Nano Omni 30B A3B Reasoning",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano Omni is NVIDIA's open-weight multimodal model that takes video, audio, images and text and returns text, a hybrid Mamba-Transformer mixture of experts with about 31B parameters and 3B active, published in BF16, FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json",
      "dns": "nemotron-3-nano-omni-30b-a3b-reasoning.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
      "license": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron3"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21). The card's download section still shows an hf auth login step, which the ungated files don't need.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model Agreement. Running it yourself costs the hardware, 66.1 GB for the BF16 files, 35.2 GB for FP8 and 22.4 GB for NVFP4, per the Hub's file lists. NVIDIA's own API also serves it (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "omni",
        "vision",
        "audio",
        "video",
        "reasoning",
        "tool-calling",
        "speech-to-text",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61.2,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1350,
        "categoryRank": 27,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.",
        "bestFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools",
        "strengths": [
          "Video, audio, images and text in one model, with word-level timestamps for transcription per the card",
          "Released support in vLLM, SGLang, llama.cpp, Ollama and LM Studio, plus NVIDIA's own FP8 and NVFP4 builds",
          "Tool calling with a named parser and NVIDIA cookbooks, and thinking that turns off or takes a budget per request",
          "Training data listed by dataset with sample and token counts",
          "Commercial use under an irrevocable licence with no use policy or threshold, ungated, and served by NVIDIA's own API"
        ],
        "weaknesses": [
          "Loads with trust_remote_code in every documented runtime command",
          "English only, no speaker diarisation, translation or streaming input, and video capped at 2 minutes",
          "No published safety results and no agent-specific guidance such as prompt-injection advice",
          "None of the 11 questions in its three repositories has a reply",
          "The NVFP4 build needs NVIDIA Blackwell, and SGLang supports only the BF16 build so far, per the card"
        ],
        "agentNotes": [
          "Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent",
          "Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB",
          "Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video",
          "Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends",
          "Render PDF pages to PNG and send them as images, since the API takes no raw PDF",
          "Treat transcripts and text read from images or video as untrusted input before acting on them"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61.2
          }
        ],
        "editorialScores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 70
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-03",
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron3\n# or, with vLLM 0.20.0 or later as the card gives it:\npip install \"vllm[audio]\"\nvllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 \\\n  --trust-remote-code --reasoning-parser nemotron_v3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "parakeet-tdt-0-6b-v3",
        "nemotron-3-5-parse",
        "cosmos-reason2-2b",
        "nemotron-3-5-bolt-e4b-4gb",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
        "params": "31B",
        "active": "3B",
        "context": 262144,
        "goodFor": "Multimodal understanding of video with speech, documents, charts and screens, and English transcription, with tool calling and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 22.4,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell; the card's minimum is one RTX 5090 32 GB"
          },
          {
            "name": "ollama nemotron3:33b-nvfp4",
            "url": "https://ollama.com/library/nemotron3:33b-nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 21,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "MLX build (digest f263b909bfad, the same as 33b-mlx), so Apple silicon only. Size as Ollama's tags page shows it, rounded to whole GB"
          },
          {
            "name": "LM Studio nvidia/nemotron-3-nano-omni Q4_K_M",
            "url": "https://huggingface.co/lmstudio-community/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 26.1,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "Q4_K_M GGUF of 24.52 GB plus the 1.59 GB BF16 vision projector, the build behind LM Studio's catalogue entry"
          },
          {
            "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 35.2,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "The card says SGLang support for FP8 is still to come"
          },
          {
            "name": "ollama nemotron3:33b-q4_K_M",
            "url": "https://ollama.com/library/nemotron3:33b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 28,
            "memoryGB": 48,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "digest baa676a14e13, text and image input per the tags page. Size as Ollama's tags page shows it, rounded to whole GB"
          },
          {
            "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.1,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "Includes 7.3 MB of sample media; loads with trust_remote_code"
          }
        ],
        "run": "vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --video-pruning-rate 0.5 --media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}' --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8",
        "runBuild": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "params": "30B total, 3B active (31.6B per the safetensors index)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b"
          },
          {
            "name": "Nemotron 3 Nano 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano 4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
            "params": "550B total, 55B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B GenRM",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3.5 Bolt 74B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16",
            "params": "74B total, 9B active"
          },
          {
            "name": "Nemotron 3.5 Bolt E4B 4GB",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "params": "8B total, 4B effective",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb"
          },
          {
            "name": "Nemotron Labs 3 Elastic 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16",
            "params": "75B total, 9B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math SFT",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math RL",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron Labs 3 Competitive Coding 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4",
            "params": "550B total, 55B active"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
            "params": "0.6B (638M per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b"
          },
          {
            "name": "Nemotron 3.5 Parse",
            "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse",
            "params": "0.631B (630,800,496 trainable parameters per the card)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse"
          }
        ],
        "olderCount": 47,
        "unrestrictedCopies": 5
      },
      "alsoIn": [
        "speech-to-text"
      ],
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card says the model was developed by NVIDIA, and the licence names NVIDIA Corporation. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.",
          "Nothing is hosted for this listing, so the NVIDIA Open Model Agreement stands in for terms of service and no privacy policy is scored.",
          "The changelog is the BF16 repository's commit history, 30 commits from 20 April to 24 August 2026. Some messages say what changed (Transformers 5.6 support on 27 April, batched audio on 28 April, the C-RADIOv4-H encoder reference on 8 May); the latest reads Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form, and NVIDIA PSIRT publishes a disclosure policy and bulletins."
        ],
        "score": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json",
      "live": {
        "slug": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-04T15:54:28.709726124Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-01T14:14:07.135656692Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-04T08:26:31.836280428Z",
            "changedAt": "2026-11-04T08:26:31.836280428Z",
            "fingerprint": "69cc4cb26f87"
          },
          {
            "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-11-04T08:43:00.389577943Z",
            "changedAt": "2026-10-29T05:25:34.28731257Z",
            "fingerprint": "35918cb1a524"
          }
        ],
        "updatedAt": "2026-11-04T15:54:28.709726124Z"
      }
    },
    "answer": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.",
    "b": {
      "slug": "ripple-65b-preview",
      "name": "Ripple-65B Preview",
      "vendor": "Ricursive Intelligence",
      "vendorUrl": "https://www.ricursive.com/ripple",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.",
      "url": "https://www.anchorterminal.com/tools/ripple-65b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json",
      "dns": "ripple-65b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ricursiveAI/Ripple-65B-Preview"
        },
        {
          "registry": "huggingface",
          "name": "ricursiveAI/Ripple-65B-Preview-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-04).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-04"
      },
      "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "reasoning",
        "tool-calling",
        "coding",
        "apache-2.0",
        "safetensors",
        "remote-code",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-11-04",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.3,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1350,
        "categoryRank": 78,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 45,
          "risk": 55,
          "upkeep": 49
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.",
        "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.",
        "strengths": [
          "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to",
          "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately",
          "Trained for long context at 2M-token sequences, with 1M stated",
          "Base model and a speculative-decoding draft published beside the instruct model",
          "Safetensors only, with a checksum on every file"
        ],
        "weaknesses": [
          "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code",
          "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF",
          "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run",
          "Thinking can't be turned off or budgeted",
          "No safety guidance for agent use, no security contact and no knowledge cut-off"
        ],
        "agentNotes": [
          "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default",
          "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags",
          "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields",
          "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on",
          "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.3
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 45,
          "risk": 55,
          "upkeep": 49
        },
        "provenanceScore": 36,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n  --model-path ../.. \\\n  --speculative-draft-model-path ../../../ripple-dflash2",
        "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ripple-65b-preview"
      },
      "weights": {
        "trainedBy": "Ricursive Intelligence",
        "params": "65B",
        "context": 1048576,
        "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ripple-65B-Preview-NVFP4",
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 57.2,
            "memoryGB": 69,
            "format": "safetensors",
            "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day"
          },
          {
            "name": "Ripple-65B-Preview",
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 129.6,
            "memoryGB": 160,
            "format": "safetensors",
            "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs"
          }
        ],
        "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2",
        "runBuild": "Ripple-65B-Preview",
        "checked": "2026-11-04",
        "minMemoryGB": 69
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Ricursive Intelligence, Inc.",
        "domain": "ricursive.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-04",
        "notes": [
          "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.",
          "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.",
          "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.",
          "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.",
          "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one."
        ],
        "score": 36
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.",
          "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read."
        },
        "scores": {
          "a": 85,
          "b": 45
        }
      },
      {
        "by": 21,
        "category": "upkeep",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.",
          "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M."
        },
        "scores": {
          "a": 70,
          "b": 49
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Agent interface",
        "reasons": {
          "a": "A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).",
          "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M."
        },
        "scores": {
          "a": 90,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Risk",
        "reasons": {
          "a": "File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.",
          "b": "File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw."
        },
        "scores": {
          "a": 60,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "b": "Ricursive Intelligence",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-24",
        "b": "2026-11-04",
        "name": "Last release"
      },
      {
        "a": "no date given",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Ripple-65B Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 45",
          "Risk, 60 against 55",
          "Agent interface, 90 against 75",
          "Documentation and upkeep, 70 against 49"
        ],
        "also": null,
        "goodFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools",
        "slug": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "watchFor": "Loads with trust_remote_code in every documented runtime command"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.",
        "slug": "ripple-65b-preview",
        "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code"
      }
    ],
    "history": {
      "nemotron-3-nano-omni-30b-a3b-reasoning": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-03",
          "delta": 0.5,
          "from": 60.7,
          "fromGrade": "C",
          "to": 61.2,
          "toGrade": "C"
        }
      ],
      "ripple-65b-preview": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.json",
        "title": "Agents-A1-4B vs Ripple-65B Preview",
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json",
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        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2"
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        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Cohere Transcribe 03-2026 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0",
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        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 85,
        "ripple-65b-preview": 45,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 60,
        "ripple-65b-preview": 55,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 90,
        "ripple-65b-preview": 75,
        "weight": 10
      },
      {
        "by": 21,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 70,
        "ripple-65b-preview": 49,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "nemotron-3-nano-omni-30b-a3b-reasoning": "A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.",
      "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest."
    }
  },
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  "links": {
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  "markdown": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 61.2/100, rank #36 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json\n- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3674 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 254 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning (C)\n\nGood for: Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools\n\nAhead on:\n- Reach, 85 against 45\n- Risk, 60 against 55\n- Agent interface, 90 against 75\n- Documentation and upkeep, 70 against 49\n\nWatch for: Loads with trust_remote_code in every documented runtime command\n\n### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Reach: Nemotron 3 Nano Omni 30B A3B Reasoning +40\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (85): Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.\n- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n### Documentation and upkeep: Nemotron 3 Nano Omni 30B A3B Reasoning +21\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (70): The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.\n- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n### Agent interface: Nemotron 3 Nano Omni 30B A3B Reasoning +15\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (90): A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).\n- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n### Risk: Nemotron 3 Nano Omni 30B A3B Reasoning +5\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (60): File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.\n- Ripple-65B Preview (55): File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 45 | Nemotron 3 Nano Omni 30B A3B Reasoning +40 |\n| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |\n| Agent interface | 10% (10 this run) | 90 | 75 | Nemotron 3 Nano Omni 30B A3B Reasoning +15 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 49 | Nemotron 3 Nano Omni 30B A3B Reasoning +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.2 · C** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Ricursive Intelligence |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-24 | 2026-11-04 |\n| Terms last updated | no date given | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not found in the text | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n- +0.5 on 3 November 2026: correction\n\n### Ripple-65B Preview\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.\n\n**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\n\n## Before you call either\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n1. Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent\n2. Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB\n3. Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video\n4. Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends\n5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF\n6. Treat transcripts and text read from images or video as untrusted input before acting on them\n\n### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Ripple-65B Preview?\n\nNemotron 3 Nano Omni 30B A3B Reasoning scores 61.2 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-omni-30b-a3b-reasoning\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare nemotron-3-nano-omni-30b-a3b-reasoning ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with Nemotron 3 Nano Omni 30B A3B Reasoning or Ripple-65B Preview\n\n- [Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Agents-A1-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Apertus v1.5 8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B 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