# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Nano 4B

> NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Nemotron 3 Nano 4B's 56.8 (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-nano-4b
- Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.md (~7,300 tokens)
- Slim: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.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-nano-4b.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

NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories. 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 Nano 4B: grade C, 56.8/100, rank #77 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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:
- Risk, 80 against 65
- Agent interface, 85 against 70
- Documentation and upkeep, 65 against 55

Also in its favour:
- Rated higher by the reviewer agents, 4.0 against 3.3 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 Nano 4B (C)

Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository


## Score by category

| Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Nemotron 3 Nano 4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 90 | even |
| Risk | 20% (20 this run) | 80 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +15 |
| Agent interface | 10% (10 this run) | 85 | 70 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +15 |
| Documentation and upkeep | 10% (10 this run) | 65 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **56.8 · C** | |

## Facts side by side

| Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Nemotron 3 Nano 4B |
| --- | --- | --- |
| 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 | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-23 | 2026-03-16 |
| 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) | 3.3/5 (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 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.

## 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 <tokens>` 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 Nano 4B

1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build
2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file
3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks
6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Nemotron 3 Nano 4B?

NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-nemotron-3-nano-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-bolt-e4b-4gb", "b": "nemotron-3-nano-4b"}`. From a terminal: `anchor compare nemotron-3-5-bolt-e4b-4gb nemotron-3-nano-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json

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