How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf apolo13x/Cosmos-Reason2-8B-GGUF:
# Run inference directly in the terminal:
llama-cli -hf apolo13x/Cosmos-Reason2-8B-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf apolo13x/Cosmos-Reason2-8B-GGUF:
# Run inference directly in the terminal:
llama-cli -hf apolo13x/Cosmos-Reason2-8B-GGUF:
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 apolo13x/Cosmos-Reason2-8B-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf apolo13x/Cosmos-Reason2-8B-GGUF:
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 apolo13x/Cosmos-Reason2-8B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf apolo13x/Cosmos-Reason2-8B-GGUF:
Use Docker
docker model run hf.co/apolo13x/Cosmos-Reason2-8B-GGUF:
Quick Links

Cosmos-Reason2-8B-GGUF

GGUF quantizations of nvidia/Cosmos-Reason2-8B for use with llama.cpp and compatible tools.

About the Model

NVIDIA Cosmos Reason 2 is an open, 8B-parameter reasoning vision-language model (VLM) for physical AI and robotics. It is post-trained from Qwen3-VL-8B-Instruct and understands space, time, and fundamental physics.

Key capabilities:

  • Physical AI reasoning with spatio-temporal understanding
  • Object detection with 2D/3D point localization and bounding boxes
  • Long-context understanding up to 256K input tokens
  • Video analytics, data curation, and robot planning

For full details, see the original model card.

Quantization Details

File Quant Size
Cosmos-Reason2-8B-F16.gguf F16 16 GB
Cosmos-Reason2-8B-Q8_0.gguf Q8_0 8.2 GB
Cosmos-Reason2-8B-Q4_K_M.gguf Q4_K_M 4.7 GB
mmproj-Cosmos-Reason2-8B-F16.gguf F16 1.1 GB

Note: The vision encoder (mmproj) is kept at F16 precision.

How to Use

llama-server -hf Kbenkhaled/Cosmos-Reason2-8B-GGUF:Q8_0
llama-server -hf Kbenkhaled/Cosmos-Reason2-8B-GGUF:F16
llama-server -hf Kbenkhaled/Cosmos-Reason2-8B-GGUF:Q4_K_M
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GGUF
Model size
8B params
Architecture
qwen3vl
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