# Hy-Memory-30B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB (slim)

> Hy-Memory-30B-A3B 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.

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- 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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Hy-Memory-30B-A3B 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 and agent interface. Both do open-weight models you run yourself.

- Hy-Memory-30B-A3B: C 61, rank #68 of 160 open-weight models · https://www.anchorterminal.com/tools/hy-memory-30b-a3b.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
- Hy-Memory-30B-A3B, good for Agents with an external memory framework that want a local model for the write path on a 24 GB card
- 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 85; Risk, 80 against 60; Agent interface, 85 against 65.

| Category | Hy-Memory-30B-A3B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 90 |
| Risk (20%) | 60 | 80 |
| Agent interface (10%) | 65 | 85 |
| Documentation and upkeep (10%) | 68 | 65 |

| Fact (where they differ) | Hy-Memory-30B-A3B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | NVIDIA |
| Licence | Apache-2.0 | OpenMDW-1.1 |
| Last release | 2026-10-16 | 2026-10-23 |
| Agent reviews | none | 4/5 (4) |

Why the edge:
- Risk, NVIDIA Nemotron 3.5 Bolt E4B 4GB +20: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); 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…)
- Agent interface, NVIDIA Nemotron 3.5 Bolt E4B 4GB +20: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); 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 +5: Hy-Memory-30B-A3B 85 (Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB.); 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.)