{
  "data": {
    "a": {
      "slug": "nemotron-3-nano-30b-a3b",
      "name": "Nemotron 3 Nano 30B A3B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json",
      "dns": "nemotron-3-nano-30b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3-nano:30b"
        }
      ],
      "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).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 GB for Ollama's default build (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": "2026-11-01"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "reasoning",
        "tool-calling",
        "commercial-licence",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 67.6,
        "grade": "B",
        "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": 1518,
        "categoryRank": 9,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 80
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.",
        "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "strengths": [
          "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers",
          "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted",
          "1M tokens of input with no position-encoding scaling",
          "Full model card and a detailed data card with cut-offs, with much of the training data released"
        ],
        "weaknesses": [
          "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository",
          "No safety or bias results published, though the subcards say both were run",
          "No NVIDIA reply on any of the nine questions others opened since 1 March 2026, including a chat-template report",
          "Licence replaced eight days after release with no dated note, and the subcards still link the old one",
          "Structured output isn't documented, and the custom licence isn't OSI-approved"
        ],
        "agentNotes": [
          "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)",
          "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`",
          "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151",
          "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text",
          "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning",
          "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks",
          "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens",
          "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3",
          "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution",
          "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 67.6
          }
        ],
        "editorialScores": {
          "capability": 46,
          "interface": 90,
          "reach": 90,
          "risk": 60,
          "upkeep": 80
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "lastMoved": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3-nano:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n  --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"nemotron-3-nano:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-nano-30b-a3b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "nemotron-3-5-transcribe-30b-a4b",
        "parakeet-tdt-0-6b-v3",
        "parakeet-unified-en-0-6b",
        "nemotron-3-5-parse",
        "cosmos-reason2-2b",
        "nemotron-3-5-bolt-e4b-4gb",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "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-Base-BF16",
        "params": "30B",
        "active": "3.5B",
        "context": 1048576,
        "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "ollama nemotron-3-nano:30b",
            "url": "https://ollama.com/library/nemotron-3-nano:30b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 24,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "digest 7487f38ecf24. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "ggml-org NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF",
            "who": "llama.cpp",
            "precision": "q4_k_m",
            "sizeGB": 22.42,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 22.42 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.7,
            "memoryGB": 48,
            "format": "safetensors"
          },
          {
            "name": "ggml-org NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3-Nano-30B-A3B-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 33.59,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 33.59 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 63.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3",
        "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "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 Nano Omni 30B-A3B Reasoning",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning"
          },
          {
            "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"
          },
          {
            "name": "Nemotron 3.5 Transcribe 30B-A4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B",
            "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b"
          }
        ],
        "olderCount": 47,
        "unrestrictedCopies": 5
      },
      "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-nemotron-open-model-license/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.",
          "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.json",
      "live": {
        "slug": "nemotron-3-nano-30b-a3b",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:00.271598775Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-08T15:03:11.895084282Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:18.236938919Z",
            "changedAt": "2026-11-10T16:23:18.236938919Z",
            "fingerprint": "d16f84ee772a"
          },
          {
            "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-11-10T16:43:25.039992347Z",
            "changedAt": "2026-10-28T04:52:55.000219692Z",
            "fingerprint": "093132ed6fed"
          }
        ],
        "updatedAt": "2026-11-10T16:43:25.039992347Z"
      }
    },
    "answer": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in every scored category.",
    "b": {
      "slug": "otel-2-0-llm-31b-it",
      "name": "OTel-2.0-LLM-31B-IT",
      "vendor": "AT\u0026T (Open Telco AI)",
      "vendorUrl": "https://github.com/farbodtavakkoli/OTel",
      "kind": "weights",
      "category": "open-weights",
      "summary": "OTel-2.0-LLM-31B-IT is AT\u0026T's open-weight telecoms model, Gemma 4 31B IT post-trained on about 440 billion telecom tokens from GSMA's corpus of standards, for RAG and question answering over 3GPP, ETSI and other specifications, published in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json",
      "dns": "otel-2-0-llm-31b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "farbodtavakkoli/OTel-2.0-LLM-31B-IT"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata gives it, with Google's Gemma 4 licence page as the link. Running it yourself costs the hardware, 62.6 GB in BF16, which the card puts on two 80 GB GPUs or one of 192 GB (checked 2026-11-10).",
      "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 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "fine-tune",
        "telecom",
        "domain-specific",
        "rag",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-11-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.8,
        "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": 1518,
        "categoryRank": 83,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 74
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.",
        "bestFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware",
        "strengths": [
          "The card states what the training covered and left out, and what hasn't been validated",
          "Training sources and sizes are named, with GSMA as the provider of the corpus",
          "Apache 2.0, ungated, safetensors only, no code to run",
          "A full vLLM serving guide with sizing, stop tokens and troubleshooting",
          "The owner answers questions on the repository"
        ],
        "weaknesses": [
          "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked",
          "The files are in a personal account, not an AT\u0026T or GSMA organisation",
          "Weights replaced in place three times, and an old shard set left in the repository",
          "No tool-call format, structured output or thinking",
          "Post-trained at 4,096 tokens, so long-context use is unproven",
          "No safety results and no security contact"
        ],
        "agentNotes": [
          "Download the 15 shards the index names, not the whole repository, which is twice the size",
          "Pin a revision. The card says the weights may change under the same name",
          "Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's",
          "Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel",
          "Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review",
          "Don't use it to run network tools unattended. The card says that hasn't been evaluated"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 74
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# with vLLM 0.19.0 or later, on two 80 GB GPUs, per the card:\nvllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --served-model-name OTel-2.0-LLM-31B-IT \\\n  --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16 --port 8000",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"OTel-2.0-LLM-31B-IT\", \"messages\": [{\"role\": \"user\", \"content\": \"What model is this?\"}], \"temperature\": 0}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/otel-2-0-llm-31b-it"
      },
      "weights": {
        "trainedBy": "AT\u0026T Chief Data Office, with GSMA, Red Hat, Microsoft, AMD, Dell, MLCommons and Pleias named as contributors",
        "builtOn": "google/gemma-4-31b-it (post-trained with OSFT; named on the card and in its metadata)",
        "params": "31B",
        "context": 262144,
        "goodFor": "Telecoms question answering and RAG over 3GPP, ETSI, GSMA, CAMARA, ITU, O-RAN and TM Forum material, in English, as the generation stage with retrieval and human review around it.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "OTel-2.0-LLM-31B-IT",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.55,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The 15 shards the index names. The repository also holds an earlier 14-shard set of 64.21 GB, so download by the index and not the whole repository. No quantised build from the publisher or the usual converters"
          }
        ],
        "run": "vllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16",
        "runBuild": "OTel-2.0-LLM-31B-IT",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "AT\u0026T (Open Telco AI)",
        "family": "OTel 2.0",
        "url": "https://www.anchorterminal.com/open-weights/#att-open-telco",
        "others": [
          {
            "name": "OTel-2.0-LLM-31B-IT-QLoRA",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT-QLoRA",
            "params": "31B"
          },
          {
            "name": "OTel-2.0-Decision-26B-A4B-4bit",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-Decision-26B-A4B-4bit",
            "params": "26B total, 4B active (per name; card not read)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "AT\u0026T (the card and GSMA's newsroom name AT\u0026T as the releaser, and the model answers that AT\u0026T Chief Data Office trained it). Published from a contributor's personal accounts on Hugging Face and GitHub",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No vendor domain is recorded. The project's pages are a personal GitHub repository and Hugging Face account, and open-telco.ai, the site GSMA's article links, answered 403 on 10 November 2026.",
          "GSMA's newsroom article of 24 July 2026 names AT\u0026T as the releaser and GSMA as the source of the corpus. It doesn't link the Hugging Face repository.",
          "The Hub lists the publishing account as a member of the AT\u0026T and GSMA organisations.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The GitHub repository has no SECURITY.md (404 on 10 November 2026)."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.json"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.",
          "b": "Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50."
        },
        "scores": {
          "a": 90,
          "b": 50
        }
      },
      {
        "by": 30,
        "category": "interface",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.",
          "b": "Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60."
        },
        "scores": {
          "a": 90,
          "b": 60
        }
      },
      {
        "by": 6,
        "category": "upkeep",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.",
          "b": "Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74."
        },
        "scores": {
          "a": 80,
          "b": 74
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "nemotron-3-nano-30b-a3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.",
          "b": "File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT\u0026T's or GSMA's site links to: GSMA's article names AT\u0026T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55."
        },
        "scores": {
          "a": 60,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "b": "AT\u0026T (Open Telco AI)",
        "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 Nemotron Open Model License (custom, Apache-style, 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": "2026-11-01",
        "name": "Last release"
      },
      {
        "a": "2025-12-15",
        "b": "no document linked",
        "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": "3.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, Nemotron 3 Nano 30B A3B or OTel-2.0-LLM-31B-IT?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 50",
          "Risk, 60 against 55",
          "Agent interface, 90 against 60",
          "Documentation and upkeep, 80 against 74"
        ],
        "also": null,
        "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.",
        "slug": "nemotron-3-nano-30b-a3b",
        "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware",
        "slug": "otel-2-0-llm-31b-it",
        "watchFor": "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked"
      }
    ],
    "history": {
      "nemotron-3-nano-30b-a3b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-01",
          "delta": 0.5,
          "from": 67.1,
          "fromGrade": "B",
          "to": 67.6,
          "toGrade": "B"
        }
      ],
      "otel-2-0-llm-31b-it": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.json",
        "title": "Agents-A1-4B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.json",
        "title": "Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it.json",
        "title": "Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Arete-27B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.json",
        "title": "Arete-27B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it"
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      {
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-30b-a3b.json",
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-nano-30b-a3b": 90,
        "otel-2-0-llm-31b-it": 50,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-nano-30b-a3b": 60,
        "otel-2-0-llm-31b-it": 55,
        "weight": 20
      },
      {
        "by": 30,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-nano-30b-a3b": 90,
        "otel-2-0-llm-31b-it": 60,
        "weight": 10
      },
      {
        "by": 6,
        "edge": "nemotron-3-nano-30b-a3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-nano-30b-a3b": 80,
        "otel-2-0-llm-31b-it": 74,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
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    "verdicts": {
      "nemotron-3-nano-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.",
      "otel-2-0-llm-31b-it": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented."
    }
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  "markdown": "Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano 30B A3B: grade B, 67.6/100, rank #12 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json\n- OTel-2.0-LLM-31B-IT: grade C, 56.8/100, rank #111 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Reach, 90 against 50\n- Risk, 60 against 55\n- Agent interface, 90 against 60\n- Documentation and upkeep, 80 against 74\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n### OTel-2.0-LLM-31B-IT (C)\n\nGood for: Telecoms teams that want an open model for standards questions and RAG on their own hardware\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n\n## Why the edge\n\n### Reach: Nemotron 3 Nano 30B A3B +40\n\n- Nemotron 3 Nano 30B A3B (90): Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today.\n- OTel-2.0-LLM-31B-IT (50): Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n\n### Agent interface: Nemotron 3 Nano 30B A3B +30\n\n- Nemotron 3 Nano 30B A3B (90): The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling.\n- OTel-2.0-LLM-31B-IT (60): Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n\n### Documentation and upkeep: Nemotron 3 Nano 30B A3B +6\n\n- Nemotron 3 Nano 30B A3B (80): The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply.\n- OTel-2.0-LLM-31B-IT (74): Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.\n\n### Risk: Nemotron 3 Nano 30B A3B +5\n\n- Nemotron 3 Nano 30B A3B (60): File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins.\n- OTel-2.0-LLM-31B-IT (55): File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT\u0026T's or GSMA's site links to: GSMA's article names AT\u0026T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55.\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano 30B A3B | OTel-2.0-LLM-31B-IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 50 | Nemotron 3 Nano 30B A3B +40 |\n| Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano 30B A3B +5 |\n| Agent interface | 10% (10 this run) | 90 | 60 | Nemotron 3 Nano 30B A3B +30 |\n| Documentation and upkeep | 10% (10 this run) | 80 | 74 | Nemotron 3 Nano 30B A3B +6 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **67.6 · B** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano 30B A3B | OTel-2.0-LLM-31B-IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | AT\u0026T (Open Telco AI) |\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 Nemotron Open Model License (custom, Apache-style, 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 | 2026-11-01 |\n| Terms last updated | 2025-12-15 | no document linked |\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 | 3.3/5 (4) | none |\n\n## How the scores have moved\n\n### Nemotron 3 Nano 30B A3B\n\n- +0.5 on 1 November 2026: correction\n\n### OTel-2.0-LLM-31B-IT\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n**OTel-2.0-LLM-31B-IT.** A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.\n\n## Before you call either\n\n### Nemotron 3 Nano 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n### OTel-2.0-LLM-31B-IT\n\n1. Download the 15 shards the index names, not the whole repository, which is twice the size\n2. Pin a revision. The card says the weights may change under the same name\n3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's\n4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel\n5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review\n6. Don't use it to run network tools unattended. The card says that hasn't been evaluated\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano 30B A3B or OTel-2.0-LLM-31B-IT?\n\nNemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), 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-30b-a3b-vs-otel-2-0-llm-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-otel-2-0-llm-31b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-30b-a3b\", \"b\": \"otel-2-0-llm-31b-it\"}`. 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