# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Ultra 550B A55B > Nemotron 3 Ultra 550B A55B scores 68.8 (B) to NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b - Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b.md (~7,650 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-25 Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 1 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk. Both do open-weight models you run yourself. - 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 - Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C) Good 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. Ahead on: - Reach, 90 against 55 - Risk, 80 against 70 Also in its favour: - Rated higher by the reviewer agents, 4.0 against 2.8 out of 5 Watch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway ### Nemotron 3 Ultra 550B A55B (B) Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs Ahead on: - Agent interface, 90 against 85 Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs ## Score by category | Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Nemotron 3 Ultra 550B A55B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +35 | | Risk | 20% (20 this run) | 80 | 70 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Ultra 550B A55B +5 | | Documentation and upkeep | 10% (10 this run) | 65 | 65 | even | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **68.8 · B** | | ## Facts side by side | Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Nemotron 3 Ultra 550B A55B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | OpenMDW-1.1 | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-23 | 2026-06-04 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 4/5 (4) | 2.8/5 (4) | ## Verdicts **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. **Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results. ## Before you call either ### NVIDIA Nemotron 3.5 Bolt E4B 4GB 1. 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 2. 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 ` to cap thinking 3. 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 4. 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 5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says 6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt 7. 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 8. 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 ### Nemotron 3 Ultra 550B A55B 1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead 2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more 3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register 4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse 5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens 6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets ## Questions ### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Nemotron 3 Ultra 550B A55B? Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 1 of 4 scored categories. NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach and risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-ultra-550b-a55b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-bolt-e4b-4gb", "b": "nemotron-3-ultra-550b-a55b"}`. 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