{ "data": { "a": { "slug": "nemotron-3-5-bolt-e4b-4gb", "name": "NVIDIA Nemotron 3.5 Bolt E4B 4GB", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3.5 Bolt E4B 4GB is NVIDIA's open-weight small language model for local voice assistants, interactive characters and device agents, post-trained from Google's Gemma 4 E4B, with 8B parameters (4B effective) and a 64K-token context.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16" }, { "registry": "ollama", "name": "nemotron-3.5-bolt:e4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB, NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 and both DFlash drafter repositories (checked 2026-10-24).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 for NVIDIA's post-training and Apache 2.0 for the Gemma 4 base. Running it costs the hardware, 6.18 GB for NVIDIA's Q4_K_M GGUF and 16.06 GB for the BF16 repository. NVIDIA's API catalogue also serves it as nvidia/nemotron-3.5-bolt-e4b, which this listing doesn't grade (checked 2026-10-24).", "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 2026-10-24).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-24" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "gemma4", "fine-tune", "reasoning", "tool-calling", "conversational", "edge", "gguf", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-23", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1210, "categoryRank": 37, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-24" }, "negative": 0, "verdict": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.", "bestFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.", "strengths": [ "NVIDIA's Q4_K_M GGUF is 6.18 GB, and NVIDIA measured 3.5 to 5 GB of VRAM in use on RTX and Jetson", "Released support in llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio", "Trained for multi-step tool use and instruction hierarchy, with the gemma4 parser named and several calls per turn", "Thinking switches per request and can be budgeted, and conversational_mode asks for brief spoken replies", "OpenMDW-1.1 on NVIDIA's part and Apache 2.0 on the Gemma 4 base, no gate, and the RL datasets published", "DFlash drafters for llama.cpp and vLLM, which NVIDIA says speed decoding by up to 2.7 times" ], "weaknesses": [ "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway", "No safety evaluation results published", "One quantised build from NVIDIA, and its GGUF is text only with no vision or audio projector", "The card says thinking is on by default while the template defaults to off", "Independent results so far cover instruction following only (IFBench and MultiChallenge)" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K", "With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking", "With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja", "Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do", "Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says", "Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt", "Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says", "Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-10-24", "reach": "2026-10-24", "risk": "2026-10-24", "upkeep": "2026-10-24" }, "last": "2026-10-24", "oldest": "2026-10-24", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3.5-bolt:e4b\n# or, with llama.cpp and NVIDIA's own GGUF (the card's recipe, without Docker):\nhf download nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --local-dir bolt\nllama-server -m bolt/Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000\n# or, with vLLM 0.31.0 or later on the BF16 weights:\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 --served-model-name nemotron-bolt --max-model-len 65536 \\\n --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 \\\n --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nemotron-bolt\", \"messages\": [{\"role\": \"user\", \"content\": \"What is 17 multiplied by 23?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-5-bolt-e4b-4gb" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "nemotron-3-5-parse", "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", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA (post-training), on Google's Gemma 4 E4B", "builtOn": "google/gemma-4-E4B-it (reinforcement-learning post-training, declared in the BF16 card's metadata; base Apache 2.0)", "params": "8B", "active": "4B effective", "context": 65536, "goodFor": "A small, fast cousin for voice and device assistants, game characters and robot interfaces that interleave short replies with multi-turn tool calls on a consumer GPU or a Jetson.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.18, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf, 6,180,446,208 bytes, imatrix-calibrated on about 1M tokens of RL rollouts. Text only, no vision or audio projector. Replaced on 23 October 2026 to set the context metadata to 65,536 tokens" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit", "who": "lm-studio", "precision": "4bit", "sizeGB": 4.23, "memoryGB": 8, "format": "mlx", "runsOn": [ "LM Studio" ], "note": "The smallest 4-bit build from a runtime's library, 4,231,265,397 bytes for the repository. LM Studio's catalogue page also lists its Q4_K_M, Q6_K and Q8_0 GGUFs and an 8-bit MLX build, and gives 5 GB as minimum memory" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 6.18, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "6,180,446,208 bytes, a different file from NVIDIA's by checksum. Q6_K is 6.22 GB and Q8_0 8.03 GB" }, { "name": "ollama nemotron-3.5-bolt:e4b-q4_K_M", "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 7.9, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Size as Ollama's tags page shows it. The e4b tag is 7.2 to 7.9 GB by platform, with text and image input. Ollama shows a 128K context, above NVIDIA's 64K" }, { "name": "ollama nemotron-3.5-bolt:e4b-nvfp4", "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 7.2, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama (MLX)" ], "note": "MLX build for Apple silicon with a 64K context, size as Ollama's tags page shows it. Ollama also lists e4b-mxfp8 (12 GB), e4b-q8_0 (9.8 GB) and e4b-bf16 (17 GB)" }, { "name": "ggml-org NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 8.03, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation, with a BF16 GGUF (15.05 GB) and a DFlash NVFP4 drafter GGUF. No 4-bit build there" }, { "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 16.06, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM", "Transformers", "SGLang" ], "note": "Whole repository, 15.99 GB of weights. The card intends it mainly for fine-tuning and evaluation, and its vLLM recipe sets the image, video and audio limits to 0" } ], "run": "llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000", "runBuild": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)", "checked": "2026-10-24", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 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" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" } ], "olderCount": 47 }, "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-Bolt-E4B-4GB/commits/main", "securityTxt": "unknown", "checked": "2026-10-24", "notes": [ "The card names NVIDIA Corporation as model developer. The files sit in the verified nvidia organisation on Hugging Face, the one developer.nvidia.com and the card's blog link to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licences stand in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repositories' commit history. Since release only the cards changed, apart from the GGUF replaced on release day to set its context to 65,536 tokens.", "www.nvidia.com/.well-known/security.txt stays unknown, as recorded for the other Nemotron listings. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.json", "live": { "slug": "nemotron-3-5-bolt-e4b-4gb", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-25T15:27:25.017341078Z" }, "updatedAt": "2026-10-25T15:27:25.017341078Z" } }, "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category.", "b": { "slug": "qwen3-omni-30b-a3b-instruct", "name": "Qwen3-Omni-30B-A3B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-Omni-30B-A3B-Instruct is Alibaba's open-weight omni-modal mixture of experts, a Thinker that takes text, images, audio and video and a Talker that answers in text or speech, about 35B parameters with 3B active, published in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.json", "repo": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Omni-30B-A3B-Instruct" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the card's metadata says `other` with license_name apache-2.0, and the GitHub repository's LICENSE is Apache 2.0). Running it yourself costs the hardware, 70.5 GB for the BF16 files, the only build Alibaba publishes; the card's own table puts BF16 inference at 78.85 GB for a 15-second video (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/Qwen/Qwen3-Omni-30B-A3B-Instruct", "capabilities": [ "speech.stt", "speech.languages", "speech.voices", "voice.speech-to-speech", "voice.tools", "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "omni", "vision", "video", "audio", "speech-to-speech", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2025-09-22", "graded": true, "anchor": { "graded": true, "score": 41.8, "grade": "E", "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": 1210, "categoryRank": 81, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 24, "interface": 60, "reach": 55, "risk": 55, "upkeep": 26 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.", "bestFor": "Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice", "strengths": [ "Takes text, images, audio and video in one prompt and answers in text or speech, in 10 speech output languages", "Plain Apache 2.0 with no gate, no account and no use policy", "Safetensors only, with no code in the repository and a checksum on every file", "Released vLLM, SGLang and llama.cpp run its Thinker, and Transformers runs the whole model", "Trained for tool calls from spoken requests, several in one turn" ], "weaknesses": [ "Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba", "Speech output only in Transformers or vLLM-Omni, and Transformers is slow on this mixture of experts", "No hosted provider for this checkpoint", "No change to the files or code in over a year and no reply from Qwen to any of the 40 most recent discussions", "No safety results, agent guidance or security contact, a 65,536-token context and no thinking mode in this edition" ], "agentNotes": [ "For text answers, `mkdir -p media \u0026\u0026 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path \"$(pwd)/media\"` with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API", "The card's command passes `--allowed-local-media-path /`, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path", "Send audio as `audio_url` and images as `image_url` content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately", "For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz", "Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time", "Parse tool calls as JSON inside `\u003ctool_call\u003e` tags yourself, since no parser is named, and confirm actions triggered by speech before running them", "Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise", "Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "E", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 41.8 } ], "editorialScores": { "capability": 24, "interface": 60, "reach": 55, "risk": 55, "upkeep": 26 }, "provenanceScore": 49, "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": "# Thinker only, text out, with vLLM 0.11.1 or later, with a dedicated media folder rather than the card's /:\nmkdir -p media\nvllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path \"$(pwd)/media\"\n# speech out needs Transformers 4.57.0 or later:\npip install \"transformers\u003e=4.57.0\" accelerate qwen-omni-utils", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"audio_url\", \"audio_url\": {\"url\": \"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/demo/cough.wav\"}}, {\"type\": \"text\", \"text\": \"What can you hear?\"}]}]}'" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/qwen3-omni-30b-a3b-instruct" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "35.3B (3B active)", "context": 65536, "goodFor": "Omni-modal assistant that listens, watches and reads, then answers in text or speech, for voice agents, transcription, translation and audio-visual questions.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 70.5, "memoryGB": 96, "format": "safetensors", "note": "Released vLLM serves the Thinker only, text out; speech output needs Transformers, whose card table puts 15 seconds of video at 78.85 GB" } ], "run": "vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path \"$(pwd)/media\"", 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}, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 2 }, "alsoIn": [ "voice-agents" ], "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (per the pilot's RDAP check of qwen.ai; this model's files name no copyright holder)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The weights repository has no LICENSE file; the card's metadata says license other with license_name apache-2.0, and the GitHub repository's LICENSE is Apache 2.0. The legal entity and domain date are carried from the pilot's RDAP check of qwen.ai on 20 October 2026.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The GitHub README has a dated news list (release on 22 September 2025), and the Hugging Face commit messages are uploads, README updates and one config update on 22 September 2025.", "No security.txt was found for qwen.ai, as the pilot recorded." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.json", "live": { "slug": "qwen3-omni-30b-a3b-instruct", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:28:54.526754219Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-10-25T13:51:56.4259776Z" }, "updatedAt": "2026-10-25T15:28:54.526754219Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "OpenMDW-1.1", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-23", "b": "2025-09-22", "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": "4/5 (4)", "b": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category.", "question": "Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Qwen3-Omni-30B-A3B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 55", "Risk, 80 against 55", "Agent interface, 85 against 60", "Documentation and upkeep, 65 against 26" ], "also": [ "Rated higher by the reviewer agents, 4.0 against 2.0 out of 5" ], "goodFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.", "slug": "nemotron-3-5-bolt-e4b-4gb", "watchFor": "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway" }, { "aheadOn": null, "also": null, "goodFor": "Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice", "slug": "qwen3-omni-30b-a3b-instruct", "watchFor": "Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Agents-A1-4B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Apertus v1.5 8B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Apodex-1.1-mini vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "BigBang-v1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Carbon-X vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Carbon-X vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Cosmos-Reason2-2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DeepSeek-V3.2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-omni-30b-a3b-instruct.json", "title": "DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DeepSeek V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DeepSeek-V4-Flash-0731 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-omni-30b-a3b-instruct.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DiffusionGemma 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 12B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 26B A4B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 31B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 E2B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 E4B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-4.5-Air vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-omni-30b-a3b-instruct.json", "title": "GLM-4.5-Air vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-5.3-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "GLM-5.3-Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-5.3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "GLM-5.3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": 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"json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Granite 4.2 3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Hy3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Hy3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": 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"url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniMax-M2.7 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniMax-M3.1-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiniMax-M3.1-Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": 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"json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct", "url": 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"https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-asr-1-7b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "reach", "name": "Reach", "nemotron-3-5-bolt-e4b-4gb": 90, "qwen3-omni-30b-a3b-instruct": 55, "weight": 25 }, { "by": 25, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "risk", "name": "Risk", "nemotron-3-5-bolt-e4b-4gb": 80, "qwen3-omni-30b-a3b-instruct": 55, "weight": 20 }, { "by": 25, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "interface", "name": "Agent interface", "nemotron-3-5-bolt-e4b-4gb": 85, "qwen3-omni-30b-a3b-instruct": 60, "weight": 10 }, { "by": 39, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-5-bolt-e4b-4gb": 65, "qwen3-omni-30b-a3b-instruct": 26, "weight": 10 } ], "summary": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "nemotron-3-5-bolt-e4b-4gb": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.", "qwen3-omni-30b-a3b-instruct": "An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct", "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.min.md" }, "markdown": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category. Both do open-weight models you run yourself.\n\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #45 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n- Qwen3-Omni-30B-A3B-Instruct: grade E, 41.8/100, rank #109 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.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 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 398 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C)\n\nGood for: Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.\n\nAhead on:\n- Reach, 90 against 55\n- Risk, 80 against 55\n- Agent interface, 85 against 60\n- Documentation and upkeep, 65 against 26\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 4.0 against 2.0 out of 5\n\nWatch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway\n\n### Qwen3-Omni-30B-A3B-Instruct (E)\n\nGood for: Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice\n\nWatch for: Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba\n\n\n## Score by category\n\n| Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Qwen3-Omni-30B-A3B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +35 |\n| Risk | 20% (20 this run) | 80 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |\n| Agent interface | 10% (10 this run) | 85 | 60 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 26 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +39 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **41.8 · E** | |\n\n## Facts side by side\n\n| Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Qwen3-Omni-30B-A3B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | OpenMDW-1.1 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-23 | 2025-09-22 |\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 | 4/5 (4) | 2/5 (4) |\n\n## Verdicts\n\n**NVIDIA Nemotron 3.5 Bolt E4B 4GB.** A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.\n\n**Qwen3-Omni-30B-A3B-Instruct.** An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.\n\n## Before you call either\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K\n2. With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking\n3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja\n4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do\n5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says\n6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt\n7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says\n8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines\n\n### Qwen3-Omni-30B-A3B-Instruct\n\n1. For text answers, `mkdir -p media \u0026\u0026 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path \"$(pwd)/media\"` with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API\n2. The card's command passes `--allowed-local-media-path /`, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path\n3. Send audio as `audio_url` and images as `image_url` content parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately\n4. For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz\n5. Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time\n6. Parse tool calls as JSON inside `\u003ctool_call\u003e` tags yourself, since no parser is named, and confirm actions triggered by speech before running them\n7. Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise\n8. Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16\n\n## Questions\n\n### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Qwen3-Omni-30B-A3B-Instruct?\n\nNVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-5-bolt-e4b-4gb\", \"b\": \"qwen3-omni-30b-a3b-instruct\"}`. From a terminal: `anchor compare nemotron-3-5-bolt-e4b-4gb qwen3-omni-30b-a3b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json and https://www.anchorterminal.com/api/v1/tools/qwen3-omni-30b-a3b-instruct.json\n\n## Other comparisons with NVIDIA Nemotron 3.5 Bolt E4B 4GB or Qwen3-Omni-30B-A3B-Instruct\n\n- [Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Agents-A1-4B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Apertus v1.5 8B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Apodex-1.1-mini vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-omni-30b-a3b-instruct.md)\n- [BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [BigBang-v1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Carbon-X vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Carbon-X vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Cosmos-Reason2-2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Darwin-36B-Opus vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek-V3.2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- 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2602](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-voxtral-mini-4b-realtime-2602.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-25", "license": "CC-BY-4.0", "method": "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": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Omni-30B-A3B-Instruct", "url": "" } ], "description": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Qwen3-Omni-30B-A3B-Instruct's 41.8 (E) for open-weight models you run yourself. 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