Running NVIDIA’s Nemotron-Nano-12B-v2-VL-FP8 model on RunPod offers a seamless, efficient way to deploy a powerful multimodal AI without the usual setup headaches. Traditionally, testing or prototyping with advanced AI models required significant technical effort—manually configuring environments, managing GPU drivers, dealing with dependency conflicts, and facing high costs, especially when using high-end hardware locally. These challenges often made rapid experimentation impractical, particularly when working with large models that demand substantial compute resources. RunPod eliminates this friction by providing a cloud-based platform that lets users launch fully configured, GPU-powered environments in seconds. With just a few lines of code, you can spin up a session, load the model, and start running inference—ideal for testing, prototyping, or integrating AI into workflows. To run the Nemotron-Nano-12B-v2-VL-FP8 model, the process begins with installing the necessary dependencies. Using the vLLM library, which optimizes inference performance, the setup is straightforward: python pip install -q vllm Next, load the model with minimal configuration. The model is available through the Hugging Face Hub and supports FP8 quantization for efficient performance: ```python from vllm import LLM, SamplingParams llm = LLM( model="nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8", trust_remote_code=True, quantization="modelopt", max_model_len=4096, gpu_memory_utilization=0.9, ) print("Model loaded successfully!") ``` With the model ready, you can begin running inference. For example, to explain large language models in a concise way: ```python sampling_params = SamplingParams( temperature=0.6, top_p=0.9, max_tokens=512, ) messages = [ {"role": "system", "content": "/no_think"}, {"role": "user", "content": "Explain what large language models are and why they matter, in 3-4 sentences."} ] outputs = llm.chat(messages, sampling_params=sampling_params) print(outputs[0].outputs[0].text) ``` The model responds with a clear, accurate explanation—demonstrating its strong language understanding. It also handles complex coding tasks with precision. For instance, requesting a Python function to check for prime numbers: ```python messages = [ {"role": "system", "content": "/no_think"}, {"role": "user", "content": "Write a Python function that checks if a number is prime. Include a docstring."} ] outputs = llm.chat(messages, sampling_params=sampling_params) print(outputs[0].outputs[0].text) ``` The output includes a well-structured function with proper documentation, showcasing the model’s ability to generate correct, readable code. This entire workflow runs smoothly on RunPod’s infrastructure, which supports high-performance GPUs and auto-scaling, making it ideal for repeated testing and development. The platform’s pay-as-you-go model ensures cost control, allowing developers to experiment freely without fear of runaway charges. Nemotron-Nano-12B-v2-VL-FP8 excels in multimodal tasks—processing both text and visual inputs—making it valuable for use cases like document audits, fraud detection, compliance checks, and code generation. Its efficiency, accuracy, and ease of deployment on RunPod make it a compelling choice for teams building AI-powered tools.