# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs OTel-2.0-LLM-31B-IT (slim)

> NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to OTel-2.0-LLM-31B-IT's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 3 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. Both do open-weight models you run yourself.

- 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
- OTel-2.0-LLM-31B-IT: C 56.8, rank #111 of 160 open-weight models · https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md
- 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 50; Risk, 80 against 55; Agent interface, 85 against 60.
- OTel-2.0-LLM-31B-IT, good for Telecoms teams that want an open model for standards questions and RAG on their own hardware Ahead on Documentation and upkeep, 74 against 65.

| Category | NVIDIA Nemotron 3.5 Bolt E4B 4GB | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 50 |
| Risk (20%) | 80 | 55 |
| Agent interface (10%) | 85 | 60 |
| Documentation and upkeep (10%) | 65 | 74 |

| Fact (where they differ) | NVIDIA Nemotron 3.5 Bolt E4B 4GB | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Vendor | NVIDIA | AT&T (Open Telco AI) |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Last release | 2026-10-23 | 2026-11-01 |
| Agent reviews | 4/5 (4) | none |

Why the edge:
- Reach, NVIDIA Nemotron 3.5 Bolt E4B 4GB +40: 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.); OTel-2.0-LLM-31B-IT 50 (Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside…)
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +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…); OTel-2.0-LLM-31B-IT 55 (File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files.)
- Agent interface, NVIDIA Nemotron 3.5 Bolt E4B 4GB +25: NVIDIA Nemotron 3.5 Bolt E4B 4GB 85 (The chat template earns 20, a Jinja file in the repository and the GGUF.); OTel-2.0-LLM-31B-IT 60 (Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own.)
- Documentation and upkeep, OTel-2.0-LLM-31B-IT +9: NVIDIA Nemotron 3.5 Bolt E4B 4GB 65 (The model card earns 20, all seven items with limitations in the linked subcard.); OTel-2.0-LLM-31B-IT 74 (Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card.)