{ "data": { "a": { "slug": "diffusiongemma-26b-a4b-it", "name": "DiffusionGemma 26B A4B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it", "markdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json", "repo": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/diffusiongemma-26B-A4B-it" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Google also lists the model on Kaggle and in Vertex AI Model Garden.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google sells no per-call access to these weights through the Gemini API, which serves only gemma-4-31b-it and gemma-4-26b-a4b-it among Gemma models. Running them costs the hardware, 51.7 GB of BF16 safetensors; Google's inference guide asks for a GPU with more than 60 GB of memory (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/diffusiongemma.md", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "diffusion", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted", "experimental" ], "lastRelease": "2026-07-15", "graded": true, "anchor": { "graded": true, "score": 50, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 74, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 30, "interface": 70, "reach": 50, "risk": 70, "upkeep": 60 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.", "bestFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4", "strengths": [ "Apache 2.0 with nothing added, ungated, safetensors only, hashed in Google's own organisation", "Generates 256-token blocks in parallel, which Google says reaches over 1,100 tokens per second per user on an H100 in FP8 (the publisher's claim)", "Gemma 4's chat template with multi-call tool use, thinking on or off per request and a 256K context", "Runs natively in released Transformers (5.11.0 and later) and SGLang (v0.5.22), with no repository code", "The card covers intended use, limitations, architecture, parameters, context, languages and a January 2025 cut-off" ], "weaknesses": [ "No quantised build from Google, so the smallest official build needs the 64 GB band", "Not in vLLM, llama.cpp, Ollama or LM Studio", "Google's own benchmarks put it below Gemma 4 26B A4B on most tasks, τ²-bench 56.2% against 68.2%", "Tool calling isn't documented for this model, and SGLang rejects JSON Schema and grammar constraints for it", "Labelled experimental, with no hosted API and no safety figures" ], "agentNotes": [ "Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions", "In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler", "Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn", "Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself", "Streaming arrives one 256-token canvas at a time, not token by token", "Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50 } ], "editorialScores": { "capability": 30, "interface": 70, "reach": 50, "risk": 70, "upkeep": 60 }, "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": "pip install \"transformers\u003e=5.11.0\" torch accelerate\npython -c \"from transformers import DiffusionGemmaForBlockDiffusion, AutoProcessor; m='google/diffusiongemma-26B-A4B-it'; p=AutoProcessor.from_pretrained(m); model=DiffusionGemmaForBlockDiffusion.from_pretrained(m, dtype='auto', device_map='auto')\"\n# or, with SGLang v0.5.22 or later:\nsglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"google/diffusiongemma-26B-A4B-it\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"max_tokens\": 1024}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/diffusiongemma-26b-a4b-it" }, "sameCompany": [ "embeddinggemma-300m", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-26B-A4B (architecture, per the card; no base_model declared)", "params": "25.2B", "active": "3.8B", "context": 262144, "goodFor": "Fast local text generation from text, image and video prompts at low concurrency, with optional thinking and tool calls, trading some quality against Gemma 4 26B A4B for speed.", "runtimes": [ "SGLang", "Transformers" ], "builds": [ { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "who": "publisher", "precision": "bf16", "sizeGB": 51.7, "memoryGB": 64, "format": "safetensors", "runsOn": [ "Transformers", "SGLang" ], "note": "The only build from Google. Google's guide asks for more than 60 GB of GPU memory" } ], "run": "sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000", "runBuild": "diffusiongemma-26B-A4B-it", "checked": "2026-10-21", "minMemoryGB": 64 }, "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-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "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": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/google/diffusiongemma-26B-A4B-it/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date of gemma.ai are carried from the gemma-4-26b-a4b-it facts checked on 21 October 2026. gemma.ai links to huggingface.co/collections/google/diffusiongemma, which holds only this repository.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai's dated releases page has no DiffusionGemma entry. The Hugging Face commit history has four commits from 10 June to 15 July 2026 whose messages say what changed (two chat template fixes and the diffusers files).", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.json" }, "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in every scored category.", "b": { "slug": "nemotron-3-5-lightning-30b-a3b", "name": "Nemotron 3.5 Lightning 30B A3B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3.5 Lightning is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 30B parameters with 3B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3.5-lightning" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 and NVFP4 repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 65.9 GB for the BF16 repository (one H100 80GB at 256K tokens per the card), 21.6 GB for NVIDIA's NVFP4 build or 19 GB for Ollama's smallest 4-bit build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "hybrid", "reasoning", "tool-calling", "long-context", "safetensors", "nvfp4", "self-hosted", "new" ], "lastRelease": "2026-08-11", "graded": true, "anchor": { "graded": true, "score": 64.2, "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": 17, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 34, "interface": 90, "reach": 85, "risk": 75, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.", "bestFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence", "strengths": [ "Released support in vLLM, SGLang, llama.cpp, Transformers, Ollama and LM Studio", "4-bit builds of 19 GB from ggml-org and Ollama, and NVIDIA's own NVFP4 at 21.6 GB", "Safetensors only, with no repository code run to load it or parse its output", "Tool calls documented with a named parser, several per turn, and thinking off per request", "Training data listed with token counts and cut-offs, much of it published", "OpenMDW-1.1 with no gate, no threshold and no use policy, and NVIDIA's own API serves it" ], "weaknesses": [ "No safety evaluation results published", "One NVIDIA reply across 12 questions on its two repositories", "Only one quantised build from NVIDIA, and NVFP4 needs NVIDIA GPUs", "Structured output claimed but not documented, and no reasoning budget", "The card's llama.cpp recipe names a Q4_K_M file that ggml-org's repository doesn't hold" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform", "On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs", "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)", "Use `temperature=1.0` and `top_p=0.95`, as the card says", "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks", "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content", "Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.2 } ], "editorialScores": { "capability": 34, "interface": 90, "reach": 85, "risk": 75, "upkeep": 70 }, "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-3.5-lightning\n# or, with llama.cpp and the GGUF the card links:\nllama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072\n# or, with vLLM on NVIDIA GPUs (the card's command, abridged):\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 --reasoning-parser nemotron_v3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95,\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-5-lightning-30b-a3b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b" ], "weights": { "trainedBy": "NVIDIA", "params": "30B", "active": "3B", "context": 1048576, "goodFor": "Small hybrid Mamba and mixture-of-experts reasoning model for always-on agents, text only, with tool calling, a thinking switch and up to 1M tokens of context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF", "who": "llama.cpp", "precision": "q4_0", "sizeGB": 18.9, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation, which NVIDIA's card calls the official GGUF weights. Also Q8_0 (33.59 GB) and BF16 (63.18 GB), and separate MTP files of 1.16 to 4.09 GB" }, { "name": "ollama nemotron-3.5-lightning:30b-a3b-nvfp4", "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama (MLX)" ], "note": "MLX build for Apple silicon, the smallest in Ollama's library. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 21.58, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 experts with FP8 Mamba projections. Native on Blackwell, W4A16 kernels on Hopper and Ampere, per its card. Needs NVIDIA GPUs" }, { "name": "ollama nemotron-3.5-lightning:30b-a3b-q4_K_M", "url": "https://ollama.com/library/nemotron-3.5-lightning:30b-a3b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 25, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "The llama.cpp build Ollama uses on Linux. Size as Ollama's tags page shows it" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 24.52, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "The build LM Studio's catalogue page links. Also Q6_K (33.51 GB) and Q8_0 (33.59 GB)" }, { "name": "NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 65.85, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja --temp 1.0 --top-p 0.95 -c 131072", "runBuild": "ggml-org NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (Q4_0)", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron 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": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer and the LICENSE file reads Copyright 2026 NVIDIA CORPORATION \u0026 AFFILIATES. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Since release on 11 August 2026 only the cards have changed, most commits reading Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-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": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-15", "b": "2026-08-11", "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": "", "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": "2.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in every scored category.", "question": "Which is better for AI agents, DiffusionGemma 26B A4B IT or Nemotron 3.5 Lightning 30B A3B?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4", "slug": "diffusiongemma-26b-a4b-it", "watchFor": "No quantised build from Google, so the smallest official build needs the 64 GB band" }, { "aheadOn": [ "Reach, 85 against 50", "Risk, 75 against 70", "Agent interface, 90 against 70", "Documentation and upkeep, 70 against 60" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.5 out of 5" ], "goodFor": "Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence", "slug": "nemotron-3-5-lightning-30b-a3b", "watchFor": "No safety evaluation results published" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.json", "title": "Agents-A1-4B vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B", "url": 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"https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Hy3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling-Small vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kimi K3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Kolibri-1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna XS 2.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Ling-3.0-tiny vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniCPM-o 4.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniCPM5-2B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniMax-M2.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "MiniMax-M2.7 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Muse Glimmer 30B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-30b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-super-120b-a12b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0.json", "title": "Nemotron 3.5 Lightning 30B A3B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-4b-instruct-2507.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-2b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-397b-a17b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-next.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 4, "diffusiongemma-26b-a4b-it": 30, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "capability", "name": "Capability", "nemotron-3-5-lightning-30b-a3b": 34, "weight": 35 }, { "by": 35, "diffusiongemma-26b-a4b-it": 50, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "reach", "name": "Reach", "nemotron-3-5-lightning-30b-a3b": 85, "weight": 25 }, { "by": 5, "diffusiongemma-26b-a4b-it": 70, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "risk", "name": "Risk", "nemotron-3-5-lightning-30b-a3b": 75, "weight": 20 }, { "by": 20, "diffusiongemma-26b-a4b-it": 70, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "interface", "name": "Agent interface", "nemotron-3-5-lightning-30b-a3b": 90, "weight": 10 }, { "by": 10, "diffusiongemma-26b-a4b-it": 60, "edge": "nemotron-3-5-lightning-30b-a3b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-5-lightning-30b-a3b": 70, "weight": 10 } ], "summary": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in every scored category. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "diffusiongemma-26b-a4b-it": "A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.", "nemotron-3-5-lightning-30b-a3b": "A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b", "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.min.md" }, "markdown": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- DiffusionGemma 26B A4B IT: grade D, 50/100, rank #94 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json\n- Nemotron 3.5 Lightning 30B A3B: grade B, 64.2/100, rank #21 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json\n- 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### DiffusionGemma 26B A4B IT (D)\n\nGood for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4\n\nWatch for: No quantised build from Google, so the smallest official build needs the 64 GB band\n\n### Nemotron 3.5 Lightning 30B A3B (B)\n\nGood for: Agents on one 24 to 32 GB GPU or Mac that want a fast tool-calling reasoning model with long context and a permissive licence\n\nAhead on:\n- Reach, 85 against 50\n- Risk, 75 against 70\n- Agent interface, 90 against 70\n- Documentation and upkeep, 70 against 60\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: No safety evaluation results published\n\n\n## Score by category\n\n| Category | Weight | DiffusionGemma 26B A4B IT | Nemotron 3.5 Lightning 30B A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 30 | 34 | Nemotron 3.5 Lightning 30B A3B +4 |\n| Reach | 25% (25 this run) | 50 | 85 | Nemotron 3.5 Lightning 30B A3B +35 |\n| Risk | 20% (20 this run) | 70 | 75 | Nemotron 3.5 Lightning 30B A3B +5 |\n| Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3.5 Lightning 30B A3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 60 | 70 | Nemotron 3.5 Lightning 30B A3B +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50 · D** | **64.2 · B** | |\n\n## Facts side by side\n\n| Fact | DiffusionGemma 26B A4B IT | Nemotron 3.5 Lightning 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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-15 | 2026-08-11 |\n| Terms last updated | no document linked | 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 | 2.5/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**DiffusionGemma 26B A4B IT.** A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.\n\n**Nemotron 3.5 Lightning 30B A3B.** A small agent cousin, 3B active parameters, with trained parallel tool calls, a thinking switch and up to 1M tokens of context, under a permissive licence. It runs in about 23 GB from the GGUF or Ollama builds, and NVIDIA's NVFP4 build needs NVIDIA GPUs. The main risks are no published safety results and one answered question across two repositories.\n\n## Before you call either\n\n### DiffusionGemma 26B A4B IT\n\n1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions\n2. In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler\n3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn\n4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself\n5. Streaming arrives one 256-token canvas at a time, not token by token\n6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs\n\n### Nemotron 3.5 Lightning 30B A3B\n\n1. Quickest start is `ollama run nemotron-3.5-lightning`, about 19 to 25 GB of files depending on the platform\n2. On the reference machine use `llama-server -hf ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:Q4_0 --jinja`, since the card's Q4_K_M tag doesn't exist there and NVFP4 needs NVIDIA GPUs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95`, as the card says\n5. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, which vLLM's nemotron_v3 parser reads so a cut-off reasoning trace still returns content\n7. Go past 262,144 tokens only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) and enough memory\n\n## Questions\n\n### Which is better for AI agents, DiffusionGemma 26B A4B IT or Nemotron 3.5 Lightning 30B A3B?\n\nNemotron 3.5 Lightning 30B A3B scores 64.2 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-lightning-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"diffusiongemma-26b-a4b-it\", \"b\": \"nemotron-3-5-lightning-30b-a3b\"}`. From a terminal: `anchor compare diffusiongemma-26b-a4b-it nemotron-3-5-lightning-30b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-lightning-30b-a3b.json\n\n## Other comparisons with DiffusionGemma 26B A4B IT or Nemotron 3.5 Lightning 30B A3B\n\n- [Agents-A1-4B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.md)\n- [Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apertus v1.5 8B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.md)\n- [Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apodex-1.1-mini vs DiffusionGemma 26B A4B 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"https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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": "DiffusionGemma 26B A4B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "" } ], "description": "Nemotron 3.5 Lightning 30B A3B scores 64.2 (B) to DiffusionGemma 26B A4B IT's 50 (D) for open-weight models you run yourself. 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