{ "data": { "a": { "slug": "gemma-4-31b-it", "name": "Gemma 4 31B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-31b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json", "repo": "https://huggingface.co/google/gemma-4-31B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-31B-it" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-31B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:31b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, about 69.9 GB at BF16 or 17.5 GB at Q4_0 per Google's memory table. Google also hosts it on the Gemini API's free tier, a separate listing (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://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 74.1, "grade": "BB", "agentReady": true, "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": 1197, "categoryRank": 1, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.", "bestFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "strengths": [ "Google's own 17.7 GB QAT Q4_0 GGUF fits a 24 GB GPU, and Ollama's gemma4:31b is 20 GB", "Released support in llama.cpp, Ollama, vLLM, SGLang and Transformers", "Apache 2.0 with nothing added, no gate, and hosted on Google's Gemini API too", "Tool calling documented by Google with a response schema Transformers parses, several calls per turn", "Thinking that can be switched on or off per request and 262,144 tokens of native context" ], "weaknesses": [ "No safety evaluation results published", "No prompt-injection guidance and no security route tied to Gemma", "Structured output isn't documented by Google", "Training data described only in general terms", "Files unchanged since 20 July and no Google reply in the discussions since 30 July" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images", "Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build", "In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does", "Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block", "Drop earlier thoughts from the history except on tool-call turns, as the card asks", "Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64", "Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding", "The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 74.1 } ], "editorialScores": { "capability": 61, "interface": 85, "reach": 95, "risk": 70, "upkeep": 65 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gemma4:31b\n# or, with vLLM's recipe:\nvllm serve google/gemma-4-31B-it --max-model-len 32768 --enable-auto-tool-choice \\\n --reasoning-parser gemma4 --tool-call-parser gemma4", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:31b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-31b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "sameModel": [ "gemini-api" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-31B", "params": "30.7B", "context": 262144, "goodFor": "Dense general assistant and agent model with tool calling and optional thinking, taking text, images and video frames.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gemma-4-31B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf/blob/main/gemma-4-31B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 17.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Add gemma-4-31B-it-mmproj.gguf (1.2 GB) for image input" }, { "name": "ollama gemma4:31b", "url": "https://ollama.com/library/gemma4:31b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 20, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 17ba34c06c80). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "gemma-4-31B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-31B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 23.3, "memoryGB": 32, "format": "safetensors", "note": "Quantisation-aware trained, compressed-tensors format for vLLM. vLLM's quantisation table marks its Marlin INT4 kernels unsupported on AMD" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "who": "publisher", "precision": "bf16", "sizeGB": 62.5, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768", "runBuild": "gemma-4-31B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (the registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "domainNote": "RDAP shows gemma.ai registered in 2017 and transferred on 15 April 2025, so the registration date may predate Google's use of the domain.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "RDAP names Google LLC as registrant of gemma.ai, with MarkMonitor as registrar. Google's ai.google.dev pages point to gemma.ai as the current documentation.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is Google's Gemma releases page, which dates each release (Gemma 4 on 31 March 2026, its MTP drafters on 16 April). The Hugging Face commit history's messages also say what changed.", "gemma.ai/.well-known/security.txt answered 404 on 21 October 2026. www.google.com has a security.txt pointing to Google's vulnerability reward programme, which isn't linked from the card or gemma.ai." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-31b-it.json" }, "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 3 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on agent interface and documentation and upkeep.", "b": { "slug": "nemotron-3-nano-30b-a3b", "name": "Nemotron 3 Nano 30B A3B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano 30B A3B is NVIDIA's open-weight hybrid Mamba-Transformer mixture of experts for reasoning and agent work, 30B parameters with 3.5B active, published on Hugging Face in BF16, FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "license": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3-nano:30b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 63.2 GB for the BF16 files, 32.7 GB for FP8, 19.4 GB for NVFP4 and 18 GB for Ollama's default build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "reasoning", "tool-calling", "commercial-licence", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-24", "graded": true, "anchor": { "graded": true, "score": 67.1, "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": 1197, "categoryRank": 10, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 46, "interface": 90, "reach": 90, "risk": 60, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.", "bestFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.", "strengths": [ "Runs in released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers", "NVIDIA's own FP8 and NVFP4 builds beside BF16, and ungated files in NVIDIA's own organisation with a SHA-256 for every shard", "Tool calling documented end to end with a named parser, several calls to a turn, and thinking that can be turned off or budgeted", "1M tokens of input with no position-encoding scaling", "Full model card and a detailed data card with cut-offs, with much of the training data released" ], "weaknesses": [ "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository", "No safety or bias results published, though the subcards say both were run", "No NVIDIA reply on any of the 10 discussions opened since 1 March 2026, including a chat-template report", "Licence replaced eight days after release with no dated note, and the subcards still link the old one", "Structured output isn't documented, and the custom licence isn't OSI-approved" ], "agentNotes": [ "Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)", "On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`", "NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151", "Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text", "Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning", "Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks", "Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens", "In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3", "Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution", "Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 67.1 } ], "editorialScores": { "capability": 46, "interface": 90, "reach": 90, "risk": 60, "upkeep": 75 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3-nano:30b\n# or, with vLLM per NVIDIA's card (the reasoning parser is a file from the repository):\nwget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py\nvllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"nemotron-3-nano:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-nano-30b-a3b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B", "active": "3.5B", "context": 1048576, "goodFor": "Hybrid Mamba-Transformer mixture of experts for reasoning and agent work, text only, with tool calling and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "ollama nemotron-3-nano:30b", "url": "https://ollama.com/library/nemotron-3-nano:30b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 24, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "digest 7487f38ecf24. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 32.7, "memoryGB": 48, "format": "safetensors" }, { "name": "NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 63.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3", "runBuild": "NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron 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" } ], "olderCount": 43, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer, and the licence names NVIDIA. RDAP gives nvidia.com a registration date of 20 April 1993.", "Nothing is hosted for this listing, so the Nemotron Open Model License stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. A few messages say what changed (a reasoning budget example on 19 December 2025, a 1M-context vLLM example on 8 January 2026); most read Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 on 21 October 2026. Vulnerability reports go to NVIDIA PSIRT, linked from the card, which publishes a disclosure policy and security bulletins." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "NVIDIA", "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": "Apache-2.0", "b": "NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-08-24", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.8/5 (4)", "b": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 3 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Gemma 4 31B IT or Nemotron 3 Nano 30B A3B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 61 against 46", "Reach, 95 against 90", "Risk, 70 against 60" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 3.3 out of 5" ], "goodFor": "An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence", "slug": "gemma-4-31b-it", "watchFor": "No safety evaluation results published" }, { "aheadOn": [ "Agent interface, 90 against 85", "Documentation and upkeep, 75 against 65" ], "also": null, "goodFor": "Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.", "slug": "nemotron-3-nano-30b-a3b", "watchFor": "The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json", "title": "Agents-A1-4B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json", "title": "Agents-A1-4B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b.json", "title": "Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json", "title": "BigBang-v1 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.json", "title": "BigBang-v1 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b.json", "title": "Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b.json", "title": "Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b.json", "title": "DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b.json", "title": "DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-30b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it.json", "title": "Gemma 4 26B A4B IT vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-30b-a3b.json", "title": "Gemma 4 26B A4B IT vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.json", "title": "Gemma 4 31B IT vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.json", "title": "Gemma 4 31B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash.json", "title": "Gemma 4 31B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.json", "title": "Gemma 4 31B IT vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.json", "title": "Gemma 4 31B IT vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b.json", "title": "Gemma 4 31B IT vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.json", "title": "Gemma 4 31B IT vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b.json", "title": "Gemma 4 31B IT vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.json", "title": "Gemma 4 31B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.json", "title": "Gemma 4 31B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json", "title": "Gemma 4 31B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Gemma 4 31B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": 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Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-397b-a17b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-4b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-6-27b.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3.6-27B", "url": 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Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-next.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Nano 30B A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b.json", "title": "Nemotron 3 Nano 30B A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 15, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 61, "key": "capability", "name": "Capability", "nemotron-3-nano-30b-a3b": 46, "weight": 35 }, { "by": 5, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 95, "key": "reach", "name": "Reach", "nemotron-3-nano-30b-a3b": 90, "weight": 25 }, { "by": 10, "edge": "gemma-4-31b-it", "gemma-4-31b-it": 70, "key": "risk", "name": "Risk", "nemotron-3-nano-30b-a3b": 60, "weight": 20 }, { "by": 5, "edge": "nemotron-3-nano-30b-a3b", "gemma-4-31b-it": 85, "key": "interface", "name": "Agent interface", "nemotron-3-nano-30b-a3b": 90, "weight": 10 }, { "by": 10, "edge": "nemotron-3-nano-30b-a3b", "gemma-4-31b-it": 65, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-30b-a3b": 75, "weight": 10 } ], "summary": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 3 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-31b-it": "A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.", "nemotron-3-nano-30b-a3b": "A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b", "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.min.md" }, "markdown": "Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 3 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json\n- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 31B IT (BB)\n\nGood for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence\n\nAhead on:\n- Capability, 61 against 46\n- Reach, 95 against 90\n- Risk, 70 against 60\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 3.3 out of 5\n\nWatch for: No safety evaluation results published\n\n### Nemotron 3 Nano 30B A3B (B)\n\nGood for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.\n\nAhead on:\n- Agent interface, 90 against 85\n- Documentation and upkeep, 75 against 65\n\nWatch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 31B IT | Nemotron 3 Nano 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 61 | 46 | Gemma 4 31B IT +15 |\n| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 31B IT +5 |\n| Risk | 20% (20 this run) | 70 | 60 | Gemma 4 31B IT +10 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Nano 30B A3B +5 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 75 | Nemotron 3 Nano 30B A3B +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.1 · BB** | **67.1 · B** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 31B IT | Nemotron 3 Nano 30B A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | NVIDIA |\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 | Apache-2.0 | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-08-24 |\n| Terms last updated | no document linked | |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.8/5 (4) | 3.3/5 (4) |\n\n## Verdicts\n\n**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.\n\n**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.\n\n## Before you call either\n\n### Gemma 4 31B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images\n2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build\n3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does\n4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block\n5. Drop earlier thoughts from the history except on tool-call turns, as the card asks\n6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64\n7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding\n8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine\n\n### Nemotron 3 Nano 30B A3B\n\n1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)\n2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`\n3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151\n4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text\n5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning\n6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks\n7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens\n8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3\n9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution\n10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 31B IT or Nemotron 3 Nano 30B A3B?\n\nGemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Nano 30B A3B's 67.1 (B), and leads in 3 of 5 scored categories. Nemotron 3 Nano 30B A3B leads on agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-31b-it\", \"b\": \"nemotron-3-nano-30b-a3b\"}`. 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