In a groundbreaking announcement, NVIDIA revealed a sweeping series of optimizations designed to turbocharge Meta Llama 3, the cutting-edge large language model (LLM) of the future.

By harnessing the power of NVIDIA accelerated computing, developers, researchers, and businesses worldwide are primed to revolutionize innovation across a broad spectrum of applications, fostering responsible advancement in the field.

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Meta engineers embarked on a groundbreaking journey, training Llama 3 on a formidable computer cluster housing 24,576 NVIDIA H100 Tensor Core GPUs, interconnected via an NVIDIA Quantum-2 InfiniBand network. In collaboration with NVIDIA, Meta meticulously fine-tuned its network infrastructure, software frameworks, and model architectures to optimize the performance of its flagship LLM.

Pioneering the Next Frontier

Meta recently unveiled ambitious plans to expand its computational infrastructure to an unprecedented scale, with a staggering 350,000 H100 GPUs slated for deployment, marking a monumental leap forward in the realm of generative AI.

Deploying Llama 3 in Action

Accelerated iterations of Llama 3, leveraging NVIDIA GPUs, are now readily accessible across cloud environments, data centers, edge devices, and personal computers. Developers can seamlessly experiment with Llama 3 via, where it is packaged as an NVIDIA NIM microservice featuring a standardized application programming interface, facilitating universal deployment.

Empowering Businesses

Enterprises can finely calibrate Llama 3 to suit their specific needs using NVIDIA NeMo, an open-source framework tailored for LLMs, seamlessly integrated within the robust NVIDIA AI Enterprise platform. Customized models can be optimized for inference through NVIDIA TensorRT-LLM and effortlessly deployed via the NVIDIA Triton Inference Server, ensuring unparalleled performance and scalability.

Extending Reach to Devices and PCs

Llama 3’s versatility extends to NVIDIA Jetson Orin, catering to robotics and edge computing applications, enabling the creation of interactive agents reminiscent of those found in the acclaimed Jetson AI Lab. Moreover, NVIDIA RTX and GeForce RTX GPUs empower workstations and PCs, accelerating inference processes for Llama 3, with over 100 million NVIDIA-accelerated systems worldwide poised to leverage this breakthrough technology.

Unlocking Peak Performance with Llama 3

Optimizing the deployment of an LLM, such as for a chatbot, necessitates striking a delicate balance between low latency, swift reading comprehension, and efficient GPU utilization to minimize operational costs. NVIDIA’s rigorous testing indicates that a single NVIDIA H200 Tensor Core GPU can generate approximately 3,000 tokens per second, supporting up to 300 concurrent users, thus demonstrating the unparalleled efficiency of Llama 3.

Charting the Path Forward

NVIDIA remains steadfast in its commitment to advancing community-driven initiatives, pledging to optimize open-source software that empowers users to tackle their most formidable challenges. By championing open-source models, NVIDIA promotes transparency in AI and fosters collaborative efforts to enhance AI safety and resilience.

Discover More

Delve deeper into NVIDIA’s AI inference platform and explore the state-of-the-art techniques employed in NIM, TensorRT-LLM, and Triton, including groundbreaking methodologies like low-rank adaptation aimed at accelerating the latest LLMs.

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