{ "data": { "a": { "slug": "granite-speech-5-0-2b", "name": "Granite Speech 5.0 2B", "vendor": "IBM", "vendorUrl": "https://www.ibm.com/granite", "kind": "weights", "category": "speech-to-text", "summary": "Granite Speech 5.0 2B is IBM's open-weight speech model of 2.3B parameters for transcription in six languages and speech translation to and from English, with keyword lists. Apache 2.0, with IBM's own GGUF builds from about 2 GB.", "url": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b", "markdownUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json", "dns": "granite-speech-5-0-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ibm-granite/granite-speech-5.0-2b" }, { "registry": "huggingface", "name": "ibm-granite/granite-speech-5.0-2b-GGUF" }, { "registry": "ollama", "name": "ibm/granite5-speech" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The safetensors and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself costs little: 3.1 GB at Q8_0 with its audio projector, which IBM recommends, or 5.0 GB in BF16. IBM also runs a trial endpoint on Hugging Face that takes a Hugging Face token (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "speech.translation", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "speech-translation", "multilingual", "keyword-biasing", "small", "apache-2.0", "safetensors", "gguf", "signed", "self-hosted" ], "lastRelease": "2026-11-06", "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": 1518, "categoryRank": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 55, "reach": 95, "risk": 70, "upkeep": 92 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.", "bestFor": "Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware", "strengths": [ "Apache 2.0, ungated, with a signed repository", "Runs in llama.cpp, Ollama, vLLM and Transformers, each documented", "IBM's own GGUF builds, about 3 GB at the precision it recommends", "Training data listed with hours and sources", "Keyword lists of up to 2,000 terms", "Updated in the last week, and both questions on the repository answered" ], "weaknesses": [ "About two minutes of audio per request, so long recordings must be split", "Plain text out: no timestamps, segments or speaker labels", "Six languages", "Builds below Q8_0 lose accuracy on longer audio, by IBM's own account", "No safety results", "No cut-off for the training data" ], "agentNotes": [ "Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them", "Use the card's prompt for the task word for word, such as `transcribe the speech with proper punctuation and capitalization.`", "Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute", "To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms", "With vLLM, serve with `--max-model-len 2048`, as the card does", "An unknown prompt falls back to transcription, so check that the output is the task you asked for" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 55, "reach": 95, "risk": 70, "upkeep": 92 }, "provenanceScore": 88, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "brew install llama.cpp\nllama-cli -st -hf ibm-granite/granite-speech-5.0-2b-GGUF:Q8_0 --audio audio.wav -p \"transcribe the speech with proper punctuation and capitalization.\"\n# or: ollama run ibm/granite5-speech:2b, or vllm serve ibm-granite/granite-speech-5.0-2b --max-model-len 2048", "http": "# vLLM and llama-server both serve an OpenAI-compatible chat API. Send 16 kHz mono audio of up to about\n# two minutes as base64 in the message, with one of the card's task prompts as the text" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/granite-speech-5-0-2b" }, "sameCompany": [ "granite-embedding-small-english-r2", "granite-guardian", "ibm-watson-stt", "granite-switch", "granite-4-2-30b", "granite-4-2-3b", "granite-4-2-8b" ], "weights": { "trainedBy": "IBM", "builtOn": "An intermediate checkpoint of granite-4.0-1b-base, per the card", "params": "2.3B", "goodFor": "Transcribing and translating short recordings in six languages on a laptop, with a keyword list for names and jargon.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "Transformers" ], "builds": [ { "name": "granite-speech-5.0-2b-GGUF Q4_K_M", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b-GGUF/blob/main/granite-speech-5.0-2b-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 2.3, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "1.14 GB with the 1.16 GB audio projector. IBM says precisions below Q8_0 lose accuracy on audio over about a minute" }, { "name": "granite-speech-5.0-2b-GGUF Q8_0", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b-GGUF/blob/main/granite-speech-5.0-2b-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 3.12, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "1.96 GB with the 1.16 GB audio projector. The build IBM recommends, and the one in Ollama's library" }, { "name": "granite-speech-5.0-2b (BF16 safetensors)", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b/tree/main", "who": "publisher", "precision": "bf16", "sizeGB": 5.04, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Two shards" } ], "run": "llama-cli -st -hf ibm-granite/granite-speech-5.0-2b-GGUF:Q8_0 --audio audio.wav -p \"transcribe the speech with proper punctuation and capitalization.\"", "runBuild": "granite-speech-5.0-2b-GGUF Q8_0", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "IBM (Granite)", "family": "Granite Speech", "url": "https://www.anchorterminal.com/open-weights/#ibm", "others": [ { "name": "granite-speech-5.0-470m-turboctc", "url": "https://huggingface.co/ibm-granite/granite-speech-5.0-470m-turboctc", "params": "470M, per the name" }, { "name": "granite-speech-4.1-2b", "url": "https://huggingface.co/ibm-granite/granite-speech-4.1-2b", "params": "2B, per the name" } ], "olderCount": 64 }, "area": "voice", "provenance": { "legalEntity": "International Business Machines Corporation (per IBM's naming guidance, as read for granite-guardian)", "domain": "ibm.com", "domainRegistered": "1986-03-19", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ibm-granite/granite-speech-5.0-2b/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "IBM's legal entity, domain and security.txt are carried from our Granite 4.2 listings.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 28 commits from 7 October to 6 November 2026.", "The card gives 20 October 2026 as the release date." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/granite-speech-5-0-2b.json" }, "answer": "Granite Speech 5.0 2B and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 61 (C) and 60.3 (C).", "b": { "slug": "nemotron-3-5-asr-streaming-0-6b", "name": "Nemotron 3.5 ASR Streaming 0.6B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "speech-to-text", "summary": "Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open-weight streaming speech recognition model, a 600M-parameter FastConformer with an RNN-T decoder, transcribing 32 language-locales with punctuation and an optional detected-language tag at chunk sizes from 80 ms to 1.12 s, under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json", "dns": "nemotron-3-5-asr-streaming-0-6b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/nemotron-3.5-asr-streaming-0.6b" } ], "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).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 (the Linux Foundation's permissive model licence). NVIDIA sells no per-call access to these weights in this listing. Running them costs the hardware, about 2.6 GB of F32 safetensors or a 0.74 GB Q8_0 GGUF. fal, Together and DeepInfra serve it as inference providers on Hugging Face (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/docs/transformers/en/model_doc/nemotron3_5_asr", "capabilities": [ "speech.stt", "speech.streaming", "speech.batch", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "speech-to-text", "asr", "streaming", "multilingual", "rnnt", "fastconformer", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-09-10", "graded": true, "anchor": { "graded": true, "score": 60.3, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 54, "interface": 50, "reach": 70, "risk": 60, "upkeep": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "A small streaming transcription model for live audio in about 32 languages, with language detection and 80 ms to 1.12 s chunks. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NVIDIA's NeMo-Speech.cpp, and three providers host it. Timestamps aren't documented, and NVIDIA's own WERs are 23% to 26% outside its 19 transcription-ready locales.", "bestFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop", "strengths": [ "0.64B parameters, with a 0.74 GB Q8_0 GGUF from NVIDIA and F32 safetensors of 2.55 GB", "Cache-aware streaming with chunk sizes from 80 ms to 1.12 s chosen at run time", "One model for 32 working locales with automatic language detection and a language tag in the output", "Native in Transformers from v5.13.0, no remote code, and hosted by fal, Together and DeepInfra", "OpenMDW-1.1, permissive with no use policy, ungated, and NVIDIA answers most discussions" ], "weaknesses": [ "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp", "A pickled NeMo checkpoint sits beside the safetensors and NeMo's documented path loads it", "Plain text out; timestamps and confidence scores are claimed in a subcard but not documented", "Publisher WERs of 23% to 26% across the 13 broad-coverage locales, and 8 locales need fine-tuning", "No safety results, and training data without a cut-off" ], "agentNotes": [ "Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else", "With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`", "Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it", "Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate", "Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends", "Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.3 } ], "editorialScores": { "capability": 54, "interface": 50, "reach": 70, "risk": 60, "upkeep": 69 }, "provenanceScore": 76, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install \"transformers\u003e=5.13.0\"\npython -c \"from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='nvidia/nemotron-3.5-asr-streaming-0.6b')('speech.wav'))\"" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/nemotron-3-5-asr-streaming-0-6b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/nemotron-speech-streaming-en-0.6b (NVIDIA's own English model, extended to 40 locales)", "params": "0.64B", "goodFor": "Low-latency streaming transcription in 32 languages from one small model, with automatic language detection, for voice agents and many parallel streams.", "runtimes": [ "Transformers" ], "builds": [ { "name": "nemotron-3.5-asr-streaming-0.6b (safetensors)", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/model.safetensors", "who": "publisher", "precision": "fp32", "sizeGB": 2.55, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ] }, { "name": "nemotron-3.5-asr-streaming-0.6b.nemo", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.nemo", "who": "publisher", "precision": "fp32", "sizeGB": 2.37, "memoryGB": 8, "format": "nemo", "note": "NeMo checkpoint, a tar archive holding a PyTorch pickle; loads with NeMo from its main branch" }, { "name": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 0.74, "memoryGB": 8, "format": "gguf", "note": "For NVIDIA's NeMo-Speech.cpp, not llama.cpp (GGUF architecture asr). Re-converted with an embedded SentencePiece tokenizer on 10 September 2026" } ], "run": "hf download nvidia/nemotron-3.5-asr-streaming-0.6b nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --local-dir models \u0026\u0026 nemo-speech transcribe audio.wav --model models/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto", "runBuild": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 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 3.5 Bolt E4B 4GB", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "params": "8B total, 4B effective", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47 }, "area": "voice", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card says the model was developed by NVIDIA and the files sit in NVIDIA's verified nvidia organisation on Hugging Face, which developer.nvidia.com links to. The domain registration date is carried from the RDAP check made for nemotron-3-super-120b-a12b on 21 October 2026.", "Nothing is hosted in this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repository's commit history, 36 commits from 15 May to 10 September 2026, the last re-converting the GGUF with an embedded SentencePiece tokenizer.", "www.nvidia.com/.well-known/security.txt was not requested again; earlier NVIDIA listings record a 403. The card links NVIDIA's vulnerability submission page." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.json", "live": { "slug": "nemotron-3-5-asr-streaming-0-6b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:32.527836743Z", "changedAt": "2026-11-10T16:23:32.527836743Z", "fingerprint": "f23b23866a4b" } ], "updatedAt": "2026-11-10T16:23:32.527836743Z" } }, "edges": [ { "by": 25, "category": "reach", "leads": "granite-speech-5-0-2b", "name": "Reach", "reasons": { "a": "Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.", "b": "Memory earns the full 30 because NVIDIA's own Q8_0 GGUF is 0.74 GB and even the F32 safetensors need only about 3 GB. Runtimes get 5, for Transformers from v5.13.0; vLLM points to a third party's plugin, SGLang and llama.cpp have only a Parakeet encoder for multimodal models, and Ollama and LM Studio have nothing. NVIDIA's NeMo and NeMo-Speech.cpp run it but aren't among the six. Builds get 10 for one quantised build, the Q8_0 GGUF. Terms get 10 because OpenMDW-1.1 is permissive with nothing added but isn't OSI-approved. Download gets 10 for an ungated repository and hosted too gets 5 for fal, Together and DeepInfra." }, "scores": { "a": 95, "b": 70 } }, { "by": 23, "category": "upkeep", "leads": "granite-speech-5-0-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.", "b": "The card with its subcards covers intended use, limitations, architecture, parameters and languages, and misses a context length and a cut-off (14). Training data names the sources and a size range without a cut-off (5). The files last changed on 10 September 2026, and NVIDIA's NeMo-Speech.cpp changed this model's cache-aware RNNT streaming path on 24 October 2026, released in v0.3.0 on 30 October (20). NVIDIA answered 18 of the 25 questions and reports (20). It's a first release, with the GGUF replaced in place through a titled pull request (5). An unanswered report of a tokenizer fault for Greek costs file health half (5)." }, "scores": { "a": 92, "b": 69 } }, { "by": 10, "category": "risk", "leads": "granite-speech-5-0-2b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.", "b": "File safety gets 15, because the NeMo checkpoint beside the safetensors is a pickle that one documented path loads; no repository code runs. Provenance gets 15 for files in NVIDIA's verified organisation with hashes for every file. Licence risk keeps 15, since OpenMDW-1.1 has none of the deductions and the one-day metadata slip after release was a correction. The security programme gets 10 for the card's vulnerability route and NVIDIA's PSIRT bulletins. Agent guidance gets 5 for general accuracy cautions, and safety evaluation 0, since no results are published." }, "scores": { "a": 70, "b": 60 } }, { "by": 5, "category": "interface", "leads": "granite-speech-5-0-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.", "b": "Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. NVIDIA documents the audio, the language prompt and the chunk settings, and the language-tagged output with how to strip the tag in Transformers and NeMo (25). It was trained for one stream with one language prompt at a time (10). Timestamps and confidence scores are claimed in a subcard with no way shown to ask for them (5). The chat template line gets 10 because the language prompt is given only as processor or script arguments. Reasoning control and context score 0, with no reasoning and no stated context." }, "scores": { "a": 55, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "IBM", "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": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-09-10", "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": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Granite Speech 5.0 2B and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 61 (C) and 60.3 (C).", "question": "Which is better for AI agents, Granite Speech 5.0 2B or Nemotron 3.5 ASR Streaming 0.6B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 70", "Risk, 70 against 60", "Agent interface, 55 against 50", "Documentation and upkeep, 92 against 69" ], "also": null, "goodFor": "Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware", "slug": "granite-speech-5-0-2b", "watchFor": "About two minutes of audio per request, so long recordings must be split" }, { "aheadOn": null, "also": null, "goodFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop", "slug": "nemotron-3-5-asr-streaming-0-6b", "watchFor": "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp" } ], "history": { "granite-speech-5-0-2b": null, "nemotron-3-5-asr-streaming-0-6b": [ { "causes": [ "product" ], "date": "2026-11-04", "delta": 0.5, "from": 59.8, "fromGrade": "C", "to": 60.3, "toGrade": "C" } ] }, "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b.json", "title": "Cohere Transcribe 03-2026 vs Granite Speech 5.0 2B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-asr-tiny", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny.json", "title": "Granite Speech 5.0 2B vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b.json", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 Transcribe 30B-A4B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3.json", "title": "Granite Speech 5.0 2B vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b.json", "title": "Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b.json", "title": "Granite Speech 5.0 2B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.json", "title": "Granite Speech 5.0 2B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.json", "title": "Granite Speech 5.0 2B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Granite Speech 5.0 2B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Ming-3.0-audio-tiny vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-5-transcribe-30b-a4b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3.5 Transcribe 30B-A4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-5-transcribe-30b-a4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b" }, { "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-3-5-asr-streaming-0-6b-vs-underdog-bark-3.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 95, "key": "reach", "name": "Reach", "nemotron-3-5-asr-streaming-0-6b": 70, "weight": 25 }, { "by": 10, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 70, "key": "risk", "name": "Risk", "nemotron-3-5-asr-streaming-0-6b": 60, "weight": 20 }, { "by": 5, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 55, "key": "interface", "name": "Agent interface", "nemotron-3-5-asr-streaming-0-6b": 50, "weight": 10 }, { "by": 23, "edge": "granite-speech-5-0-2b", "granite-speech-5-0-2b": 92, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-5-asr-streaming-0-6b": 69, "weight": 10 } ], "summary": "Granite Speech 5.0 2B and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 61 (C) and 60.3 (C). Both do speech-to-text.", "tasks": null, "tutorials": [], "verdicts": { "granite-speech-5-0-2b": "A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.", "nemotron-3-5-asr-streaming-0-6b": "A small streaming transcription model for live audio in about 32 languages, with language detection and 80 ms to 1.12 s chunks. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NVIDIA's NeMo-Speech.cpp, and three providers host it. Timestamps aren't documented, and NVIDIA's own WERs are 23% to 26% outside its 19 transcription-ready locales." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b", "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.min.md" }, "markdown": "Granite Speech 5.0 2B and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 61 (C) and 60.3 (C). Both do speech-to-text.\n\n- Granite Speech 5.0 2B: grade C, 61/100, rank #67 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-speech-5-0-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json\n- Nemotron 3.5 ASR Streaming 0.6B: grade C, 60.3/100, rank #91 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\n\n### Granite Speech 5.0 2B (C)\n\nGood for: Agents that need to transcribe or translate short recordings in the six supported languages on modest hardware\n\nAhead on:\n- Reach, 95 against 70\n- Risk, 70 against 60\n- Agent interface, 55 against 50\n- Documentation and upkeep, 92 against 69\n\nWatch for: About two minutes of audio per request, so long recordings must be split\n\n### Nemotron 3.5 ASR Streaming 0.6B (C)\n\nGood for: Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop\n\nWatch for: Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp\n\n\n## Why the edge\n\n### Reach: Granite Speech 5.0 2B +25\n\n- Granite Speech 5.0 2B (95): Memory 30, the smallest build listed that keeps 4 bits is IBM's Q4_K_M GGUF, 1.14 GB with the 1.16 GB audio projector, and 2.30 GB × 1.2 = 2.8 GB, within 16 GB. IBM recommends Q8_0, 3.12 GB with the projector. Runtimes 20, Released Transformers 5.22.2 and vLLM 0.33.0 have the granite_speech model, llama.cpp b12242 has its converter and audio front end, and Ollama carries it as ibm/granite5-speech, which the card says was tested with v0.40.15. Builds 15, IBM publishes GGUF builds at fourteen quantised precisions, from Q2_K to Q8_0, with the audio projector. Terms 15, Apache 2.0 in the metadata and on the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, IBM runs a Hugging Face Inference Endpoint that serves this model through an OpenAI-compatible API, with the address and code on the card. 30 + 20 + 15 + 15 + 10 + 5 = 95.\n- Nemotron 3.5 ASR Streaming 0.6B (70): Memory earns the full 30 because NVIDIA's own Q8_0 GGUF is 0.74 GB and even the F32 safetensors need only about 3 GB. Runtimes get 5, for Transformers from v5.13.0; vLLM points to a third party's plugin, SGLang and llama.cpp have only a Parakeet encoder for multimodal models, and Ollama and LM Studio have nothing. NVIDIA's NeMo and NeMo-Speech.cpp run it but aren't among the six. Builds get 10 for one quantised build, the Q8_0 GGUF. Terms get 10 because OpenMDW-1.1 is permissive with nothing added but isn't OSI-approved. Download gets 10 for an ungated repository and hosted too gets 5 for fal, Together and DeepInfra.\n\n### Documentation and upkeep: Granite Speech 5.0 2B +23\n\n- Granite Speech 5.0 2B (92): Model card 17, Intended use, limitations, the architecture, the size, the 16K positions and two-minute segments, and the languages are on the card. Training data 20, the data is listed with sources and sizes: a 39-row table giving each corpus, its task, its hours and a link to where it comes from, with the synthetic sets described. Last change 20, the last commit, on 6 November 2026, updated the card, 4 days before this check. Responsiveness 20, Both discussions that aren't pull requests (21 and 28 October 2026) have a reply from an ibm-granite member. Version policy 5, granite-speech-4.1-2b stays in its own repository and 5.0 came out in a new one, which names its predecessor and gives a release date. File health 10, Config, template and card agree on the architecture and the 16K positions. 17 + 20 + 20 + 20 + 5 + 10 = 92.\n- Nemotron 3.5 ASR Streaming 0.6B (69): The card with its subcards covers intended use, limitations, architecture, parameters and languages, and misses a context length and a cut-off (14). Training data names the sources and a size range without a cut-off (5). The files last changed on 10 September 2026, and NVIDIA's NeMo-Speech.cpp changed this model's cache-aware RNNT streaming path on 24 October 2026, released in v0.3.0 on 30 October (20). NVIDIA answered 18 of the 25 questions and reports (20). It's a first release, with the GGUF replaced in place through a titled pull request (5). An unanswered report of a tokenizer fault for Greek costs file health half (5).\n\n### Risk: Granite Speech 5.0 2B +10\n\n- Granite Speech 5.0 2B (70): File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in either repository. Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the model does with a prompt it doesn't know: it ignores an unfamiliar or malformed prompt and transcribes, which it says limits how audio can steer the system. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the Granite 4.2 3B listing on 4 November 2026 (5). 25 + 15 + 0 + 10 + 15 + 5 = 70.\n- Nemotron 3.5 ASR Streaming 0.6B (60): File safety gets 15, because the NeMo checkpoint beside the safetensors is a pickle that one documented path loads; no repository code runs. Provenance gets 15 for files in NVIDIA's verified organisation with hashes for every file. Licence risk keeps 15, since OpenMDW-1.1 has none of the deductions and the one-day metadata slip after release was a correction. The security programme gets 10 for the card's vulnerability route and NVIDIA's PSIRT bulletins. Agent guidance gets 5 for general accuracy cautions, and safety evaluation 0, since no results are published.\n\n### Agent interface: Granite Speech 5.0 2B +5\n\n- Granite Speech 5.0 2B (55): Chat template 20, chat_template.jinja in the repository (193 characters). Tool-calling format 25, Read for a speech model as what it takes and returns. Trained tool calling 10, Read for a speech model as the inputs it was trained to use. Structured output 0, Read for a speech model as typed outputs. Reasoning control 0, A speech model with no reasoning. Context 0, the language model is extended to 16K positions, per the card and config.json (16,384), below the 32K band, and the card says to send audio in segments of about two minutes. 20 + 25 + 10 + 0 + 0 + 0 = 55.\n- Nemotron 3.5 ASR Streaming 0.6B (50): Read for a speech model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. NVIDIA documents the audio, the language prompt and the chunk settings, and the language-tagged output with how to strip the tag in Transformers and NeMo (25). It was trained for one stream with one language prompt at a time (10). Timestamps and confidence scores are claimed in a subcard with no way shown to ask for them (5). The chat template line gets 10 because the language prompt is given only as processor or script arguments. Reasoning control and context score 0, with no reasoning and no stated context.\n\n## Score by category\n\n| Category | Weight | Granite Speech 5.0 2B | Nemotron 3.5 ASR Streaming 0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 70 | Granite Speech 5.0 2B +25 |\n| Risk | 20% (20 this run) | 70 | 60 | Granite Speech 5.0 2B +10 |\n| Agent interface | 10% (10 this run) | 55 | 50 | Granite Speech 5.0 2B +5 |\n| Documentation and upkeep | 10% (10 this run) | 92 | 69 | Granite Speech 5.0 2B +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **60.3 · C** | |\n\n## Facts side by side\n\n| Fact | Granite Speech 5.0 2B | Nemotron 3.5 ASR Streaming 0.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | IBM | 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 | no | no |\n| Last release | 2026-11-06 | 2026-09-10 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## How the scores have moved\n\n### Granite Speech 5.0 2B\n\nNo score change since it was listed.\n\n### Nemotron 3.5 ASR Streaming 0.6B\n\n- +0.5 on 4 November 2026: product\n\n## Verdicts\n\n**Granite Speech 5.0 2B.** A small, well-kept speech model with a clean licence: six languages, translation, keyword lists, IBM's own GGUF and an Ollama entry, a signed repository and training data listed corpus by corpus. It handles about two minutes of audio at a time and returns plain text, with no timestamps or speakers, and publishes no safety results.\n\n**Nemotron 3.5 ASR Streaming 0.6B.** A small streaming transcription model for live audio in about 32 languages, with language detection and 80 ms to 1.12 s chunks. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NVIDIA's NeMo-Speech.cpp, and three providers host it. Timestamps aren't documented, and NVIDIA's own WERs are 23% to 26% outside its 19 transcription-ready locales.\n\n## Before you call either\n\n### Granite Speech 5.0 2B\n\n1. Split recordings into segments of up to about two minutes of 16 kHz mono audio before sending them\n2. Use the card's prompt for the task word for word, such as `transcribe the speech with proper punctuation and capitalization.`\n3. Use Q8_0. IBM says lower precisions lose accuracy on audio over about a minute\n4. To spell names and jargon right, add a keyword list to the prompt, up to 2,000 terms\n5. With vLLM, serve with `--max-model-len 2048`, as the card does\n6. An unknown prompt falls back to transcription, so check that the output is the task you asked for\n\n### Nemotron 3.5 ASR Streaming 0.6B\n\n1. Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else\n2. With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`\n3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it\n4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate\n5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends\n6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n## Questions\n\n### Which is better for AI agents, Granite Speech 5.0 2B or Nemotron 3.5 ASR Streaming 0.6B?\n\nGranite Speech 5.0 2B and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other on agent readiness, 61 (C) and 60.3 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"granite-speech-5-0-2b\", \"b\": \"nemotron-3-5-asr-streaming-0-6b\"}`. From a terminal: `anchor compare granite-speech-5-0-2b nemotron-3-5-asr-streaming-0-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-speech-5-0-2b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json\n\n## Other comparisons with Granite Speech 5.0 2B or Nemotron 3.5 ASR Streaming 0.6B\n\n- [Cohere Transcribe 03-2026 vs Granite Speech 5.0 2B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-granite-speech-5-0-2b.md)\n- [Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-asr-tiny.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-flash.md)\n- [Granite Speech 5.0 2B vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-ming-3-0-audio-tiny.md)\n- [Granite Speech 5.0 2B vs Nemotron 3.5 Transcribe 30B-A4B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-transcribe-30b-a4b.md)\n- [Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite Speech 5.0 2B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-tdt-0-6b-v3.md)\n- [Granite Speech 5.0 2B vs Parakeet Unified EN 0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-parakeet-unified-en-0-6b.md)\n- [Granite Speech 5.0 2B vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-0-6b.md)\n- [Granite Speech 5.0 2B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-asr-1-7b.md)\n- [Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Granite Speech 5.0 2B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-underdog-bark-3.md)\n- [Granite Speech 5.0 2B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-voxtral-mini-4b-realtime-2602.md)\n- [Ming-3.0-asr-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-audio-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3.5 Transcribe 30B-A4B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-5-transcribe-30b-a4b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Parakeet Unified EN 0.6B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-unified-en-0-6b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-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": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B", "url": "" } ], "description": "Granite Speech 5.0 2B and Nemotron 3.5 ASR Streaming 0.6B score within a point of each other for speech-to-text, 61 and 60.3 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Granite Speech 5.0 2B C 61", "Nemotron 3.5 ASR Streaming 0.6B C 60.3", "scores" ], "h1": "Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b", "published": "2026-10-01", "section": "tools", "title": "Granite Speech 5.0 2B vs Nemotron 3.5 ASR Streaming 0.6B (2026)", "toc": null, "updated": "2026-11-10", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-5-asr-streaming-0-6b" }, "tokens": { "markdown": 4300, "slim": 830 }, "version": 1 }