Granite 4.2 3B vs Nemotron 3 Nano 4B

Granite 4.2 3B

A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

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.

Granite 4.2 3B

1. Start it with ollama run granite4.2:3b; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072
2. With vLLM 0.32.0 or later, serve ibm-granite/granite-4.2-3b with --reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice; that release has the parser built in, so the card's --reasoning-parser-plugin ./granite_thinking_parser.py isn't needed
3. With SGLang 0.5.18 or later, --reasoning-parser auto --tool-call-parser auto resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass enable_thinking false for direct answers or low_effort true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (truncate_history_thinking), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with python -m model_signing verify sigstore and IBM's identity, per IBM's signature guide

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