{ "data": { "a": { "slug": "hy3", "name": "Hy3", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "open-weights", "summary": "Hy3 is Tencent's open-weight mixture-of-experts language model, 295B parameters with 21B active, a 256K-token context and per-request reasoning effort, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/hy3", "markdownUrl": "https://www.anchorterminal.com/tools/hy3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy3.json", "repo": "https://huggingface.co/tencent/Hy3", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/Hy3" }, { "registry": "huggingface", "name": "tencent/Hy3-FP8" }, { "registry": "huggingface", "name": "AngelSlim/Hy3-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither tencent/Hy3 nor tencent/Hy3-FP8 is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 597.6 GB for the BF16 repository, 299.9 GB for FP8 and 89.4 GB for the smallest GGUF from Tencent's AngelSlim team (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/tencent/Hy3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 62.6, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1210, "categoryRank": 22, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 57, "interface": 75, "reach": 70, "risk": 60, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.", "bestFor": "Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0", "strengths": [ "Plain Apache 2.0, downloadable with no account, safetensors only with a SHA-256 for every shard", "Model code in released Transformers, vLLM, SGLang and llama.cpp, with no remote code to run", "Several tool calls in one turn, and reasoning effort of no_think, low or high per request", "262,144 tokens of native context", "FP8 from Tencent and GPTQ-Int4 and GGUF builds from its AngelSlim team, and two live inference providers" ], "weaknesses": [ "597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build", "No safety evaluation and no prompt-injection or refusal guidance", "The card leaves out limitations, languages, knowledge cut-off and training data, and doesn't describe the tool-call format", "No change to the files since 20 July 2026, and no Tencent reply in the discussions since 22 July", "No Ollama library entry and nothing in LM Studio's hub" ], "agentNotes": [ "Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3", "In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows", "Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error", "With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content", "Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe", "Use temperature 0.9 and top_p 1.0, the card's recommended sampling", "On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers", "Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.6 } ], "editorialScores": { "capability": 57, "interface": 75, "reach": 70, "risk": 60, "upkeep": 56 }, "provenanceScore": 60, "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": "# Tencent's command from the card (8 GPUs, H20-3e or larger)\nvllm serve tencent/Hy3 --tensor-parallel-size 8 \\\n --tool-call-parser hy_v3 --reasoning-parser hy_v3 \\\n --enable-auto-tool-choice --served-model-name hy3\n# or AngelSlim's IQ1_M GGUF (89.4 GB) with llama.cpp:\nllama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"hy3\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.9, \"top_p\": 1.0, \"chat_template_kwargs\": {\"reasoning_effort\": \"high\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/hy3" }, "sameCompany": [ "hy-memory" ], "weights": { "trainedBy": "Tencent (Hy team)", "params": "295B", "active": "21B", "context": 262144, "goodFor": "Mixture-of-experts language model with tool calling and per-request reasoning effort (off by default), text only.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Hy3-Q2_K_XL", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q2_K_XL.gguf", "who": "AngelSlim", "precision": "q2_k_xl", "sizeGB": 100.1, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-IQ1_M", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-IQ1_M.gguf", "who": "AngelSlim", "precision": "iq1_m", "sizeGB": 89.4, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-Q3_K_M", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q3_K_M.gguf", "who": "AngelSlim", "precision": "q3_k_m", "sizeGB": 134.6, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-Q4_K_M", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q4_K_M.gguf", "who": "AngelSlim", "precision": "q4_k_m", "sizeGB": 182.3, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-GPTQ-Int4", "url": "https://huggingface.co/AngelSlim/Hy3-GPTQ-Int4", "who": "AngelSlim", "precision": "gptq-int4", "sizeGB": 168.5, "memoryGB": 256, "format": "safetensors", "note": "vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "Hy3-FP8", "url": "https://huggingface.co/tencent/Hy3-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 299.9, "memoryGB": 512, "format": "safetensors" }, { "name": "Hy3", "url": "https://huggingface.co/tencent/Hy3", "who": "publisher", "precision": "bf16", "sizeGB": 597.6, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3", "runBuild": "Hy3-FP8", "checked": "2026-10-21", "minMemoryGB": 128 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "Hy3", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "Hy3-preview-Base", "url": "https://huggingface.co/tencent/Hy3-preview-Base", "params": "295B total, 21B activated (+3.8B MTP)" }, { "name": "Hy3-preview", "url": "https://huggingface.co/tencent/Hy3-preview", "params": "295B total, 21B activated (+3.8B MTP)" } ], "olderCount": 22, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text. No fuller legal name was found.", "The card's official website is aistudio.tencent.com (Hy AI Studio). RDAP at Verisign gives tencent.com a registration date of 14 September 1998.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The Hugging Face commit messages are mostly uploads; the last change, on 20 July 2026, added a dtype line to config.json.", "security.txt answered 404 on www.tencent.com, and aistudio.tencent.com answers /.well-known/security.txt with its web app, so there is none." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy3.json", "live": { "slug": "hy3", "securityTxt": { "url": "https://tencent.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-25T15:28:45.853573413Z" }, "domain": { "domain": "tencent.com", "registered": "1998-09-14", "source": "https://rdap.verisign.com/com/v1/domain/tencent.com", "checkedAt": "2026-10-25T13:55:02.012482508Z" }, "updatedAt": "2026-10-25T15:28:45.853573413Z" } }, "answer": "Hy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep.", "b": { "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" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Tencent (Hy team)", "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": "2026-07-20", "b": "2026-10-23", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "4/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Hy3 or NVIDIA Nemotron 3.5 Bolt E4B 4GB?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0", "slug": "hy3", "watchFor": "597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build" }, { "aheadOn": [ "Reach, 90 against 70", "Risk, 80 against 60", "Agent interface, 85 against 75", "Documentation and upkeep, 65 against 56" ], "also": [ "Rated higher by the reviewer agents, 4.0 against 2.8 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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.json", "title": "Agents-A1-4B vs Hy3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3" }, { "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/apertus-v1-5-8b-vs-hy3.json", "title": "Apertus v1.5 8B vs Hy3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3" }, { "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/apodex-1-1-mini-vs-hy3.json", "title": "Apodex-1.1-mini vs Hy3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3" }, { "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/bigbang-v1-vs-hy3.json", "title": "BigBang-v1 vs Hy3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3" }, { "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/carbon-x-vs-hy3.json", "title": "Carbon-X vs Hy3", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-hy3" }, { "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/cosmos-reason2-2b-vs-hy3.json", "title": "Cosmos-Reason2-2B vs Hy3", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3" }, { "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/darwin-36b-opus-vs-hy3.json", "title": "Darwin-36B-Opus vs Hy3", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3" }, { "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/deepseek-v3-2-vs-hy3.json", "title": "DeepSeek-V3.2 vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3" }, { "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-v4-1-flash-vs-hy3.json", "title": "DeepSeek V4.1 Flash vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3" }, { "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-flash-0731-vs-hy3.json", "title": "DeepSeek-V4-Flash-0731 vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy3" }, { "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/diffusiongemma-26b-a4b-it-vs-hy3.json", "title": "DiffusionGemma 26B A4B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": 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"https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.json", "title": "Gemma 4 E4B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3" }, { "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/glm-4-5-air-vs-hy3.json", "title": "GLM-4.5-Air vs Hy3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3" }, { "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-4-7-flash-vs-hy3.json", "title": "GLM-4.7-Flash vs Hy3", "url": 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"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/gpt-oss-120b-vs-hy3.json", "title": "gpt-oss-120b vs Hy3", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "gpt-oss-120b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3.json", "title": "gpt-oss-20b vs Hy3", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "gpt-oss-20b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy3.json", "title": "Granite 4.2 3B vs Hy3", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy3" }, { "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/hy3-vs-inkling.json", "title": "Hy3 vs Inkling", "url": "https://www.anchorterminal.com/compare/hy3-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-inkling-small.json", "title": "Hy3 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/hy3-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Hy3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-kimi-k3.json", "title": "Hy3 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/hy3-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1.json", "title": "Hy3 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-kronos.json", "title": "Hy3 vs Kronos", "url": "https://www.anchorterminal.com/compare/hy3-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1.json", "title": "Hy3 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.json", "title": "Hy3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1.json", "title": "Hy3 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json", "title": "Hy3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json", "title": "Hy3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Hy3 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.json", "title": "Hy3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm5-2b.json", "title": "Hy3 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5.json", "title": "Hy3 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.json", "title": "Hy3 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash.json", "title": "Hy3 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b.json", "title": "Hy3 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Hy3 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b.json", "title": "Hy3 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b.json", "title": "Hy3 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Hy3 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b.json", "title": "Hy3 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b.json", "title": "Hy3 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.json", "title": "Hy3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.json", "title": "Hy3 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.json", "title": "Hy3 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.json", "title": "Hy3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.json", "title": "Hy3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Hy3 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-4b-instruct-2507.json", "title": "Hy3 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b.json", "title": "Hy3 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b.json", "title": "Hy3 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-2b.json", "title": "Hy3 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b.json", "title": "Hy3 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-4b.json", "title": "Hy3 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b.json", "title": "Hy3 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-27b.json", "title": "Hy3 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-27b" }, { "json": 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"json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-Flash-Next", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-recursive-flint.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-thinkingcap-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "nemotron-3-5-bolt-e4b-4gb", "hy3": 70, "key": "reach", "name": "Reach", "nemotron-3-5-bolt-e4b-4gb": 90, "weight": 25 }, { "by": 20, "edge": "nemotron-3-5-bolt-e4b-4gb", "hy3": 60, "key": "risk", "name": "Risk", "nemotron-3-5-bolt-e4b-4gb": 80, "weight": 20 }, { "by": 10, "edge": "nemotron-3-5-bolt-e4b-4gb", "hy3": 75, "key": "interface", "name": "Agent interface", "nemotron-3-5-bolt-e4b-4gb": 85, "weight": 10 }, { "by": 9, "edge": "nemotron-3-5-bolt-e4b-4gb", "hy3": 56, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-5-bolt-e4b-4gb": 65, "weight": 10 } ], "summary": "Hy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "hy3": "Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb", "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.min.md" }, "markdown": "Hy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Hy3: grade B, 62.6/100, rank #28 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.json\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- 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\n## Which one, for what\n\n### Hy3 (B)\n\nGood for: Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0\n\nWatch for: 597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build\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 70\n- Risk, 80 against 60\n- Agent interface, 85 against 75\n- Documentation and upkeep, 65 against 56\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 4.0 against 2.8 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\n## Score by category\n\n| Category | Weight | Hy3 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 |\n| Risk | 20% (20 this run) | 60 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 |\n| Agent interface | 10% (10 this run) | 75 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.6 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Hy3 | NVIDIA Nemotron 3.5 Bolt E4B 4GB |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | 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 | 2026-07-20 | 2026-10-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 4/5 (4) |\n\n## Verdicts\n\n**Hy3.** Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.\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## Before you call either\n\n### Hy3\n\n1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3\n2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows\n3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error\n4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content\n5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe\n6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling\n7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers\n8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers\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## Questions\n\n### Which is better for AI agents, Hy3 or NVIDIA Nemotron 3.5 Bolt E4B 4GB?\n\nHy3 scores 62.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy3\", \"b\": \"nemotron-3-5-bolt-e4b-4gb\"}`. From a terminal: `anchor compare hy3 nemotron-3-5-bolt-e4b-4gb`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n\n## Other comparisons with Hy3 or NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n- [Agents-A1-4B vs Hy3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.md)\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- [Apertus v1.5 8B vs Hy3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.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- [Apodex-1.1-mini vs Hy3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.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- 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