Open-weight local LLM
Llama-3.1-Nemotron-Nano (4B)
⭐ Mac Mini M4 16GB top pick! NVIDIA fine-tune of Llama 3.1. Hybrid /think • /no_think mode — deep reasoning on demand, instant chat otherwise. ~80–120 tok/s on Apple Silicon Metal. 128K context. Apache 2.0.
Laptop ready
6 GB RAM
Q5_K_M
Smart chat with on-demand reasoning
Parameters
4B
Minimum RAM
6 GB
Model size
2.8 GB
Quantization
Q5_K_M
Can Llama-3.1-Nemotron-Nano (4B) run locally?
Llama-3.1-Nemotron-Nano (4B) is a good fit for normal laptops and compact desktops with 8 GB RAM or more.
Search for llama-3.1-nemotron-nano-4b-v1 in LM Studio or another GGUF-compatible runtime.
lmstudio-community/Llama-3.1-Nemotron-Nano-4B-v1-GGUFchatlightspeedreasoning
Install path
01
Check RAM fitMinimum 6 GB RAM. Start with the Q5_K_M quant.02
Load the modelSearch llama-3.1-nemotron-nano-4b-v1 in LM Studio.03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.Strengths
- ⭐ Excellent for Mac Mini M4 16GB
- Hybrid reasoning: activates thinking mode on hard questions only
- 128K context window at just 4B
- Apache 2.0 — truly open-source
- GGUF available via lmstudio-community
- Faster than Phi-4 Mini on Apple Silicon Metal
Limitations
- Thinking mode can be slow for simple queries (use /no_think tag)
- Coding less strong than Phi-4 Mini
- Not the best for multilingual tasks
Best use cases
- Smart chat with on-demand reasoning
- Complex Q&A and multi-step logic
- Code explanation and debugging
- Fast local assistant on Mac / Windows laptops
- Agentic tasks requiring occasional deep thinking
Capability profile
Technical notes
This model fits these next steps
Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.