{ "data": { "a": { "slug": "nemotron-3-nano-4b", "name": "Nemotron 3 Nano 4B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 4B is NVIDIA's open-weight small hybrid Mamba-2 and Transformer reasoning model for agents on edge devices, 4B parameters compressed from Nemotron Nano 9B v2, published in BF16, FP8 and GGUF under the NVIDIA Nemotron Open Model License.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json", "dns": "nemotron-3-nano-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "license": "NVIDIA Nemotron Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF" }, { "registry": "ollama", "name": "nemotron-3-nano:4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and GGUF 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, 7.96 GB for the BF16 repository, 5.28 GB for NVIDIA's FP8 build or 2.84 GB for NVIDIA's Q4_K_M GGUF (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": "2026-10-21" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "mamba", "hybrid", "small", "edge", "reasoning", "tool-calling", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-03-16", "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": 82, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.", "bestFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "strengths": [ "NVIDIA's own 2.84 GB GGUF and 5.28 GB FP8 builds, plus Ollama and LM Studio entries", "Released support in llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio", "Several tool calls per turn in the template and thinking off per request", "262,144 tokens of context", "Free commercial licence with no threshold or use policy, and no gate" ], "weaknesses": [ "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository", "No safety evaluation results published", "Tool calling and structured output aren't documented by NVIDIA", "No NVIDIA reply to any question, and no change to the files since 20 March 2026", "Empty LICENSE files and a card snippet that names a repository that isn't public", "No hosted endpoint from NVIDIA or any provider" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build", "With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file", "With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead", "Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks", "Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says", "It's tuned for English only, and validate JSON yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.8 } ], "editorialScores": { "capability": 25, "interface": 70, "reach": 90, "risk": 65, "upkeep": 55 }, "provenanceScore": 76, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3-nano:4b\n# or, with llama.cpp and NVIDIA's GGUF:\nllama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072\n# or, with the card's vLLM command (abridged):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 --max-model-len 262144 --trust-remote-code \\\n --mamba_ssm_cache_dtype float32 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"nemotron-3-nano:4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-4b" }, "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-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-Nano-9B-v2 (compressed with Nemotron Elastic; base under the NVIDIA Open Model License Agreement)", "params": "4B", "context": 262144, "goodFor": "Small English reasoning and tool-calling model for agents on edge devices and laptops, with a thinking switch and 262,144 tokens of context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-4B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 5.28, "memoryGB": 8, "format": "safetensors" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "One file, NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf" }, { "name": "ollama nemotron-3-nano:4b", "url": "https://ollama.com/library/nemotron-3-nano:4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2.8, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "The 4b tag is 2.8 GB (q4_K_M) or 2.9 GB (MLX nvfp4) by platform. Ollama also has q8_0 at 4.2 GB and bf16 at 8.0 GB. Sizes as Ollama's tags page shows them" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Nano-4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 2.84, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "The build LM Studio's catalogue page links. Also Q6_K (3.93 GB) and Q8_0 (4.23 GB)" }, { "name": "NVIDIA-Nemotron-3-Nano-4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 7.96, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja -c 131072", "runBuild": "NVIDIA-Nemotron-3-Nano-4B-GGUF (Q4_K_M)", "checked": "2026-10-21", "minMemoryGB": 8 }, "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 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": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer. 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. The last change was the inline chat template in tokenizer_config.json on 20 March 2026, and most commits read 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-nano-4b.json", "live": { "slug": "nemotron-3-nano-4b", "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-4B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:18.735571066Z", "changedAt": "2026-11-10T16:23:18.735571066Z", "fingerprint": "3b3d07e6bba4" } ], "updatedAt": "2026-11-10T16:23:18.735571066Z" } }, "answer": "Nemotron 3 Nano 4B and OTel-2.0-LLM-31B-IT score within a point of each other on agent readiness, 56.8 (C) and 56.8 (C). OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "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-4b", "name": "Reach", "reasons": { "a": "Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.", "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": 19, "category": "upkeep", "leads": "otel-2-0-llm-31b-it", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public.", "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": 55, "b": 74 } }, { "by": 10, "category": "risk", "leads": "nemotron-3-nano-4b", "name": "Risk", "reasons": { "a": "File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. 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": 65, "b": 55 } }, { "by": 10, "category": "interface", "leads": "nemotron-3-nano-4b", "name": "Agent interface", "reasons": { "a": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 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": 70, "b": 60 } } ], "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", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-03-16", "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": "3.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Nano 4B and OTel-2.0-LLM-31B-IT score within a point of each other on agent readiness, 56.8 (C) and 56.8 (C). OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "question": "Which is better for AI agents, Nemotron 3 Nano 4B or OTel-2.0-LLM-31B-IT?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 50", "Risk, 65 against 55", "Agent interface, 70 against 60" ], "also": null, "goodFor": "Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English", "slug": "nemotron-3-nano-4b", "watchFor": "The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository" }, { "aheadOn": [ "Documentation and upkeep, 74 against 55" ], "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": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/glm-4-5-air-vs-otel-2-0-llm-31b-it.json", "title": "GLM-4.5-Air vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b.json", "title": "GLM-4.7-Flash vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it.json", "title": "GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.json", "title": "GLM-5.3-Flash vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-otel-2-0-llm-31b-it.json", "title": "GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-4b.json", "title": "GLM-5.3 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-otel-2-0-llm-31b-it.json", "title": "GLM-5.3 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-4b.json", "title": "gpt-oss-120b vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-otel-2-0-llm-31b-it.json", "title": "gpt-oss-120b vs OTel-2.0-LLM-31B-IT", "url": 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"json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-4b.json", "title": "Granite 4.2 3B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b.json", "title": "Granite 4.2 8B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 8B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it" }, { "json": 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OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b.json", "title": "Laguna XS 2.1 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it.json", "title": "Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b.json", "title": "LBS-27B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.json", "title": "LBS-27B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.json", "title": "LFM3-VL-2B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-nano-4b.json", "title": "Ling-3.0-tiny vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-otel-2-0-llm-31b-it.json", "title": "Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-nano-4b.json", "title": "Ling-3.1-flash-1019 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-nano-4b" }, { "json": 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"url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint.json", "title": "Nemotron 3 Nano 4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview.json", "title": "Nemotron 3 Nano 4B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview.json", "title": "Nemotron 3 Nano 4B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 4B vs SmolLM3-3B", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2.json", "title": "Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano 4B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b.json", "title": "Nemotron 3 Nano 4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Super 120B A12B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-otel-2-0-llm-31b-it.json", "title": "Nemotron 3 Ultra 550B A55B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-otel-2-0-llm-31b-it.json", "title": "North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-otel-2-0-llm-31b-it.json", "title": "occamy-1.0 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it.json", "title": "Olmo 3 7B Think vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-35B-A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-397B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen-agentworld-35b-a3b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B", "url": 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"url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-2b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-4b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-27b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-35b-a3b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-27b.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-next.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-2b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-8b-instruct.json", "title": "OTel-2.0-LLM-31B-IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "OTel-2.0-LLM-31B-IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint.json", "title": "OTel-2.0-LLM-31B-IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.json", "title": "OTel-2.0-LLM-31B-IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview.json", "title": "OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b.json", "title": "OTel-2.0-LLM-31B-IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b.json", "title": "OTel-2.0-LLM-31B-IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash.json", "title": "OTel-2.0-LLM-31B-IT vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.json", "title": "OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json", "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b.json", "title": "OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.json", "title": "OTel-2.0-LLM-31B-IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "nemotron-3-nano-4b", "key": "reach", "name": "Reach", "nemotron-3-nano-4b": 90, "otel-2-0-llm-31b-it": 50, "weight": 25 }, { "by": 10, "edge": "nemotron-3-nano-4b", "key": "risk", "name": "Risk", "nemotron-3-nano-4b": 65, "otel-2-0-llm-31b-it": 55, "weight": 20 }, { "by": 10, "edge": "nemotron-3-nano-4b", "key": "interface", "name": "Agent interface", "nemotron-3-nano-4b": 70, "otel-2-0-llm-31b-it": 60, "weight": 10 }, { "by": 19, "edge": "otel-2-0-llm-31b-it", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-4b": 55, "otel-2-0-llm-31b-it": 74, "weight": 10 } ], "summary": "Nemotron 3 Nano 4B and OTel-2.0-LLM-31B-IT score within a point of each other on agent readiness, 56.8 (C) and 56.8 (C). OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "nemotron-3-nano-4b": "A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it", "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it.min.md" }, "markdown": "Nemotron 3 Nano 4B and OTel-2.0-LLM-31B-IT score within a point of each other on agent readiness, 56.8 (C) and 56.8 (C). OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #110 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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 4B (C)\n\nGood for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English\n\nAhead on:\n- Reach, 90 against 50\n- Risk, 65 against 55\n- Agent interface, 70 against 60\n\nWatch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin 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\nAhead on:\n- Documentation and upkeep, 74 against 55\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 4B +40\n\n- Nemotron 3 Nano 4B (90): Memory earns the full 30, since NVIDIA's own 2.84 GB GGUF needs under 4 GB with the fifth added. Runtimes earn the full 25 from released llama.cpp, vLLM, SGLang, Transformers, Ollama and LM Studio. Builds earn 15 for NVIDIA's FP8 and GGUF. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 0, as no Hugging Face provider serves it and no NVIDIA hosting was found.\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### Documentation and upkeep: OTel-2.0-LLM-31B-IT +19\n\n- Nemotron 3 Nano 4B (55): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and dates, crawled sets with token counts, a cut-off and published NVIDIA datasets, though most synthetic sizes are undisclosed. Last change earns 5, as the files last changed on 20 March 2026, and TensorRT-LLM's Nemotron-H commits since 14 October touch MoE, NVFP4 and Puzzle paths this dense model doesn't use, in release candidates or no release. Responsiveness earns 0, with no NVIDIA reply to any of six questions, re-counted on 4 November. Version policy earns 5 as a first release. File health earns 5, since the LICENSE file is empty, reports and fixes wait without a reply, and the card's Transformers snippet names a repository that isn't public.\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 4B +10\n\n- Nemotron 3 Nano 4B (65): File safety earns 15, safetensors and GGUF only but with 10 off because the documented commands pass trust_remote_code and load a parser plugin from the repository. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, no results. Agent guidance earns 10, subcards that name prompt injection and the safeguards to add, without an instruction hierarchy. Licence risk keeps all 15, since the switch to the Nemotron licence happened on release day. 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### Agent interface: Nemotron 3 Nano 4B +10\n\n- Nemotron 3 Nano 4B (70): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 10 of 25, as only the template shows the format and the parser name appears only in a serving command. Trained tool calling earns 20, with tool-use benchmarks and data and several calls per turn in the template. Structured output earns 0, not claimed or documented. Reasoning control earns 10, thinking off per request with a parser that returns the reasoning apart. Context earns 10 for 262,144 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## Score by category\n\n| Category | Weight | Nemotron 3 Nano 4B | 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 4B +40 |\n| Risk | 20% (20 this run) | 65 | 55 | Nemotron 3 Nano 4B +10 |\n| Agent interface | 10% (10 this run) | 70 | 60 | Nemotron 3 Nano 4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 55 | 74 | OTel-2.0-LLM-31B-IT +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **56.8 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano 4B | 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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-03-16 | 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 | 3.3/5 (4) | none |\n\n## Verdicts\n\n**Nemotron 3 Nano 4B.** A 3.97-billion-parameter reasoning model with trained tool calls, a thinking switch and 262,144 tokens of context, which runs in about 4 GB from NVIDIA's own GGUF. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files unchanged and questions unanswered since March 2026.\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 4B\n\n1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build\n2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file\n3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead\n5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks\n6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says\n7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented\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 4B or OTel-2.0-LLM-31B-IT?\n\nNemotron 3 Nano 4B and OTel-2.0-LLM-31B-IT score within a point of each other on agent readiness, 56.8 (C) and 56.8 (C). OTel-2.0-LLM-31B-IT leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-otel-2-0-llm-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-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-4b\", \"b\": \"otel-2-0-llm-31b-it\"}`. From a terminal: `anchor compare nemotron-3-nano-4b otel-2-0-llm-31b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json and https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json\n\n## Other comparisons with Nemotron 3 Nano 4B or OTel-2.0-LLM-31B-IT\n\n- [Agents-A1-4B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.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 Nano 4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-4b.md)\n- [Apodex-1.1-mini vs 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"methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Nemotron 3 Nano 4B vs OTel-2.0-LLM-31B-IT", "url": "" } ], "description": "Nemotron 3 Nano 4B and OTel-2.0-LLM-31B-IT score within a point of each other for open-weight models you run yourself, 56.8 and 56.8 out of 100. 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