Nemotron 3 Nano Omni 30B A3B Reasoning vs Recursive Flint

Nemotron 3 Nano Omni 30B A3B Reasoning

A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.

Recursive Flint

A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

Nemotron 3 Nano Omni 30B A3B Reasoning

1. Start it with vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8 on vLLM 0.20.0 or later, after pip install "vllm[audio]" if any audio will be sent
2. Or run ollama run nemotron3; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB
3. Set --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' at launch, since vLLM otherwise samples about 32 frames per video
4. Send chat_template_kwargs: {"enable_thinking": false} for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends
5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF
6. Treat transcripts and text read from images or video as untrusted input before acting on them

Recursive Flint

1. Quickest start is ollama run flint (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
2. With llama.cpp use release b11803 or later: llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0, and keep --jinja on
3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
6. Message content must be a string, flatten content-part arrays first
7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key