{
  "data": {
    "a": {
      "slug": "nemotron-3-5-lightning-30b-a3b",
      "name": "Nemotron 3.5 Lightning 30B A3B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3.5 Lightning is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 30B parameters with 3B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json",
      "dns": "nemotron-3-5-lightning-30b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron-3.5-lightning"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 and NVFP4 repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 65.9 GB for the BF16 repository (one H100 80GB at 256K tokens per the card), 21.6 GB for NVIDIA's NVFP4 build or 19 GB for Ollama's smallest 4-bit 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": "2026-11-02"
      },
      "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "mamba",
        "hybrid",
        "reasoning",
        "tool-calling",
        "long-context",
        "safetensors",
        "nvfp4",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-08-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.7,
        "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": 15,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 75,
          "upkeep": 75
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.",
        "bestFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence",
        "strengths": [
          "Released support in vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio",
          "4-bit builds of 19 GB from ggml-org and Ollama, and NVIDIA's own NVFP4 at 21.6 GB",
          "Safetensors only, with no repository code run to load it or parse its output",
          "Tool calls documented with a named parser, several per turn, and thinking off per request",
          "Training data listed with token counts and cut-offs, much of it published",
          "OpenMDW-1.1 with no gate, no threshold and no use policy, and NVIDIA's own API serves it"
        ],
        "weaknesses": [
          "No safety evaluation results published",
          "One NVIDIA reply across 14 questions on its two repositories",
          "Only one quantised build from NVIDIA, and NVFP4 needs NVIDIA GPUs",
          "Structured output claimed but not documented, and no reasoning budget",
          "The card's llama.cpp recipe names a Q4_K_M file that ggml-org's repository doesn't hold"
        ],
        "agentNotes": [
          "Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform",
          "On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs",
          "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)",
          "Use `temperature=1.0` and `top_p=0.95`, as the card says",
          "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks",
          "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content",
          "Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 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": 64.7
          }
        ],
        "editorialScores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 75,
          "upkeep": 75
        },
        "provenanceScore": 76,
        "checked": {
          "categories": {
            "capability": "2026-11-02",
            "interface": "2026-11-02",
            "reach": "2026-11-02",
            "risk": "2026-11-02",
            "upkeep": "2026-11-02"
          },
          "last": "2026-11-02",
          "lastMoved": "2026-11-02",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run nemotron-3.5-lightning\n# or, with llama.cpp and the GGUF the card links:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072\n# or, with vLLM on NVIDIA GPUs (the card's command, abridged):\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --reasoning-parser nemotron_v3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n    \"temperature\": 1.0, \"top_p\": 0.95,\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nemotron-3-5-lightning-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-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "params": "30B",
        "active": "3B",
        "context": 1048576,
        "goodFor": "Small hybrid Mamba and mixture-of-experts reasoning model for always-on agents, text only, with tool calling, a thinking switch and up to 1M tokens of context.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)",
            "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF",
            "who": "llama.cpp",
            "precision": "q4_0",
            "sizeGB": 18.9,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation, which NVIDIA's card calls the official GGUF weights. Also Q8_0 (33.59 GB) and BF16 (63.18 GB), and separate MTP files of 1.16 to 4.09 GB"
          },
          {
            "name": "ollama nemotron-3.5-lightning:30b-a3b-nvfp4",
            "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama (MLX)"
            ],
            "note": "MLX build for Apple silicon, the smallest in Ollama's library. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 21.58,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 experts with FP8 Mamba projections. Native on Blackwell, W4A16 kernels on Hopper and Ampere, per its card. Needs NVIDIA GPUs"
          },
          {
            "name": "ollama nemotron-3.5-lightning:30b-a3b-q4_K_M",
            "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 25,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "The llama.cpp build Ollama uses on Linux. Size as Ollama's tags page shows it"
          },
          {
            "name": "lmstudio-community NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF",
            "who": "lm-studio",
            "precision": "q4_k_m",
            "sizeGB": 24.52,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "LM Studio"
            ],
            "note": "The build LM Studio's catalogue page links. Also Q6_K (33.51 GB) and Q8_0 (33.59 GB)"
          },
          {
            "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 65.85,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072",
        "runBuild": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)",
        "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",
            "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 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 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": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card names NVIDIA Corporation as model developer and the LICENSE file reads Copyright 2026 NVIDIA CORPORATION \u0026 AFFILIATES. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.",
          "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Since release on 11 August 2026 only the cards have changed, most commits reading Update README.md.",
          "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 76
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.json",
      "live": {
        "slug": "nemotron-3-5-lightning-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-3.5-Lightning-30B-A3B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:26.657997393Z",
            "changedAt": "2026-11-10T16:23:26.657997393Z",
            "fingerprint": "b181720663ba"
          }
        ],
        "updatedAt": "2026-11-10T16:23:26.657997393Z"
      }
    },
    "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (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": 35,
        "category": "reach",
        "leads": "nemotron-3-5-lightning-30b-a3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 25, since ggml-org's 18.9 GB Q4_0 GGUF and Ollama's 19 GB nvfp4 build both need under 24 GB with the fifth added. Runtimes earn the full 25 from released vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is NVFP4. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and Fireworks hosts it.",
          "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": 85,
          "b": 50
        }
      },
      {
        "by": 30,
        "category": "interface",
        "leads": "nemotron-3-5-lightning-30b-a3b",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens.",
          "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": 20,
        "category": "risk",
        "leads": "nemotron-3-5-lightning-30b-a3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public 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": 75,
          "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": "OpenMDW-1.1",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-11",
        "b": "2026-11-01",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (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 3.5 Lightning 30B A3B or OTel-2.0-LLM-31B-IT?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 50",
          "Risk, 75 against 55",
          "Agent interface, 90 against 60"
        ],
        "also": null,
        "goodFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence",
        "slug": "nemotron-3-5-lightning-30b-a3b",
        "watchFor": "No safety evaluation results published"
      },
      {
        "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-3-5-lightning-30b-a3b": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-02",
          "delta": 0.5,
          "from": 64.2,
          "fromGrade": "B",
          "to": 64.7,
          "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-3-5-lightning-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-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-3-5-lightning-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-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-3-5-lightning-30b-a3b.json",
        "title": "Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.json",
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.json",
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        "title": "Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-5-lightning-30b-a3b": 85,
        "otel-2-0-llm-31b-it": 50,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-5-lightning-30b-a3b": 75,
        "otel-2-0-llm-31b-it": 55,
        "weight": 20
      },
      {
        "by": 30,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-5-lightning-30b-a3b": 90,
        "otel-2-0-llm-31b-it": 60,
        "weight": 10
      },
      {
        "by": 1,
        "edge": "nemotron-3-5-lightning-30b-a3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-5-lightning-30b-a3b": 75,
        "otel-2-0-llm-31b-it": 74,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3.5 Lightning 30B A3B scores 64.7 (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,
    "tutorials": [],
    "verdicts": {
      "nemotron-3-5-lightning-30b-a3b": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.",
      "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 3.5 Lightning 30B A3B scores 64.7 (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 3.5 Lightning 30B A3B: grade B, 64.7/100, rank #20 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-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 3.5 Lightning 30B A3B (B)\n\nGood for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence\n\nAhead on:\n- Reach, 85 against 50\n- Risk, 75 against 55\n- Agent interface, 90 against 60\n\nWatch for: No safety evaluation results published\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 3.5 Lightning 30B A3B +35\n\n- Nemotron 3.5 Lightning 30B A3B (85): Memory earns 25, since ggml-org's 18.9 GB Q4_0 GGUF and Ollama's 19 GB nvfp4 build both need under 24 GB with the fifth added. Runtimes earn the full 25 from released vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio. Builds earn 10, as NVIDIA's only quantised build is NVFP4. Terms earn 10, because OpenMDW-1.1 is permissive with no threshold or use policy but isn't an OSI licence. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it and Fireworks hosts it.\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 3.5 Lightning 30B A3B +30\n\n- Nemotron 3.5 Lightning 30B A3B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and shows a tool call declared and read back. Trained tool calling earns 20, with tool-use training and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off per request with runtime parsers that return the reasoning apart. Context earns 10 for up to 1M tokens.\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### Risk: Nemotron 3.5 Lightning 30B A3B +20\n\n- Nemotron 3.5 Lightning 30B A3B (75): File safety earns 25, safetensors only with no Python files, and nothing from the repository runs to load it or parse its output. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15 under OpenMDW-1.1. The security programme earns 10 for the reporting route on the card and NVIDIA's public 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 3.5 Lightning 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) | 85 | 50 | Nemotron 3.5 Lightning 30B A3B +35 |\n| Risk | 20% (20 this run) | 75 | 55 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Agent interface | 10% (10 this run) | 90 | 60 | Nemotron 3.5 Lightning 30B A3B +30 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 74 | Nemotron 3.5 Lightning 30B A3B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.7 · B** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3.5 Lightning 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 | OpenMDW-1.1 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-11 | 2026-11-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Nemotron 3.5 Lightning 30B A3B\n\n- +0.5 on 2 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 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.\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 3.5 Lightning 30B A3B\n\n1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform\n2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95`, as the card says\n5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content\n7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory\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 3.5 Lightning 30B A3B or OTel-2.0-LLM-31B-IT?\n\nNemotron 3.5 Lightning 30B A3B scores 64.7 (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-3-5-lightning-30b-a3b-vs-otel-2-0-llm-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-lightning-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-3-5-lightning-30b-a3b\", \"b\": \"otel-2-0-llm-31b-it\"}`. From a terminal: `anchor compare nemotron-3-5-lightning-30b-a3b otel-2-0-llm-31b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json\n\n## Other comparisons with Nemotron 3.5 Lightning 30B A3B or OTel-2.0-LLM-31B-IT\n\n- [Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Agents-A1-4B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.md)\n- [Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.md)\n- [Apodex-1.1-mini vs Nemotron 3.5 Lightning 30B 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Lightning 30B A3B](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Cola2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.md)\n- [Cosmos-Reason2-2B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.md)\n- [Darwin-36B-Opus vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-otel-2-0-llm-31b-it.md)\n- [Devstral Small 2 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Devstral Small 2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-otel-2-0-llm-31b-it.md)\n- [DiffusionGemma 26B A4B IT 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OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Granite 4.2 8B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.md)\n- [Hy3 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Hy3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/hy3-vs-otel-2-0-llm-31b-it.md)\n- [Inkling-Small vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Inkling-Small vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-small-vs-otel-2-0-llm-31b-it.md)\n- [Inkling vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Inkling vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3.1 Pro vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kimi K3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.md)\n- [Kolibri-1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kolibri-1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.md)\n- [Kronos vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kronos vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it.md)\n- [Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna M.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-m-1-vs-otel-2-0-llm-31b-it.md)\n- [Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna S 2.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-otel-2-0-llm-31b-it.md)\n- [Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it.md)\n- [LBS-27B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LBS-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.md)\n- [LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM2.5-2.6B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-1.7B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM3-1.7B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-Audio-2B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-Omni-2B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-otel-2-0-llm-31b-it.md)\n- 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    "title": "Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT (2026)",
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