NVIDIA is making significant strides in the field of generative AI through its RTX AI platform, which provides developers with access to NIMs (Neural Information Models) that include a broad range of models and performance-optimized inference microservices. This initiative aims to simplify the integration of AI into applications by offering optimized AI models and runtime components in containers, allowing developers to focus on their applications without worrying about data preparation and training. In parallel, Anyscale and NeuralMagic have collaborated to integrate FP8 quantization and inference into the vLLM project, achieving more than 99% accuracy recovery and up to 2x faster performance for large language models (LLMs), with a 1.8x reduction in inter-token latency. This advancement significantly reduces memory requirements and inter-token latency, enhancing the efficiency of LLMs. Additionally, LaminiAI is leveraging NVIDIA's accelerated computing platform, including CUDA cores and Tensor Cores, to optimize their Memory Tuning algorithm for GPU-agnostic scaling, enabling seamless tuning of LLMs on both NVIDIA and AMD GPUs. The Meta Llama 3 8B language model is now available as part of these advancements.