Instructions to use avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS # Run inference directly in the terminal: llama cli -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS # Run inference directly in the terminal: llama cli -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS # Run inference directly in the terminal: ./llama-cli -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS # Run inference directly in the terminal: ./build/bin/llama-cli -hf avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
Use Docker
docker model run hf.co/avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
- LM Studio
- Jan
- Ollama
How to use avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf with Ollama:
ollama run hf.co/avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
- Unsloth Desktop
- Docker Model Runner
How to use avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf with Docker Model Runner:
docker model run hf.co/avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
- Lemonade
How to use avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull avar6/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf:IQ4_XS
Run and chat with the model
lemonade run user.NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-gguf-IQ4_XS
List all available models
lemonade list
- Atomic Chat
Llamacpp mainline compatible gguf quants of nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16. Note that this is NOT the instruct tuned model, its nvidia's base checkpoint. These quants are made using the same scheme as Aes Sedai's optimized quants for nemotron ultra instruct. He graciously provided the imatrix and commands used for these. Though as of this commit (10June2026) llamacpp still needs to be patched in order to make nemotron ultra ggufs
With chat completions, this model has some artifacts and strings that show up in the chat but it responds well enough to turn based chatting. Using text completions in with the below instruct json, the model actually reaponds normally. Tested in silly tavern without thinking
{
"input_sequence": "<|im_start|>user",
"output_sequence": "<|im_start|>assistant\n<think></think>{{name}}:",
"last_output_sequence": "",
"system_sequence": "<|im_start|>system",
"stop_sequence": "<|im_end|>",
"wrap": true,
"macro": true,
"names_behavior": "none",
"activation_regex": "",
"first_output_sequence": "",
"skip_examples": false,
"output_suffix": "<|im_end|>\n",
"input_suffix": "<|im_end|>\n",
"system_suffix": "<|im_end|>\n",
"user_alignment_message": "",
"system_same_as_user": false,
"last_system_sequence": "",
"first_input_sequence": "",
"last_input_sequence": "",
"sequences_as_stop_strings": true,
"story_string_prefix": "",
"story_string_suffix": "",
"extensions": {},
"name": "ChatML - Super3 No Reasoning"
}
- Downloads last month
- 165
4-bit