# LFM3-Omni-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB (slim)

> LFM3-Omni-2B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb.md (~10,500 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb.json · canonical https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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LFM3-Omni-2B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). 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.

- LFM3-Omni-2B: C 61, rank #71 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md
- NVIDIA Nemotron 3.5 Bolt E4B 4GB: C 61, rank #49 of 160 open-weight models · https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.min.md
- LFM3-Omni-2B, good for Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools
- NVIDIA Nemotron 3.5 Bolt E4B 4GB, 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 80; Risk, 80 against 40; Agent interface, 85 against 70; Documentation and upkeep, 65 against 49.

| Category | LFM3-Omni-2B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 80 | 90 |
| Risk (20%) | 40 | 80 |
| Agent interface (10%) | 70 | 85 |
| Documentation and upkeep (10%) | 49 | 65 |

| Fact (where they differ) | LFM3-Omni-2B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Vendor | Liquid AI | NVIDIA |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | OpenMDW-1.1 |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-10-23 |
| Agent reviews | none | 4/5 (4) |

Why the edge:
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +40: LFM3-Omni-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); NVIDIA Nemotron 3.5 Bolt E4B 4GB 80 (File safety earns 25, safetensors and GGUF only, and the documented routes run no repository code (an undocumented Inference Endpoints handler.py sits in the BF…)
- Documentation and upkeep, NVIDIA Nemotron 3.5 Bolt E4B 4GB +16: LFM3-Omni-2B 49 (Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B)…); NVIDIA Nemotron 3.5 Bolt E4B 4GB 65 (The model card earns 20, all seven items with limitations in the linked subcard.)
- Agent interface, NVIDIA Nemotron 3.5 Bolt E4B 4GB +15: LFM3-Omni-2B 70 (Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results.); NVIDIA Nemotron 3.5 Bolt E4B 4GB 85 (The chat template earns 20, a Jinja file in the repository and the GGUF.)
- Reach, NVIDIA Nemotron 3.5 Bolt E4B 4GB +10: LFM3-Omni-2B 80 (Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line.); NVIDIA Nemotron 3.5 Bolt E4B 4GB 90 (Memory earns 30, since NVIDIA's Q4_K_M GGUF is 6.18 GB, 7.4 GB with a fifth added.)