We’re excited to announce support for the Meta Llama 3 family of models in NVIDIA TensorRT-LLM, accelerating and optimizing your LLM inference p

Turbocharging Meta Llama 3 Performance with NVIDIA TensorRT-LLM and NVIDIA Triton Inference Server

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2024-04-23 08:00:06

We’re excited to announce support for the Meta Llama 3 family of models in NVIDIA TensorRT-LLM, accelerating and optimizing your LLM inference performance. You can immediately try Llama 3 8B and Llama 3 70B—the first models in the series—through a browser user interface. Or, through API endpoints running on a fully accelerated NVIDIA stack from the NVIDIA API catalog, where Llama 3 is packaged as an NVIDIA NIM with a standard API that can be deployed anywhere.

Large language models are computationally intensive. Their size makes them expensive and slow to run, especially without the right techniques. Many optimization techniques are available, such as kernel fusion and quantization to runtime optimizations like C++ implementations, KV caching, continuous in-flight batching, and paged attention. Developers must decide which combination helps their use case. TensorRT-LLM simplifies this work.

TensorRT-LLM is an open-source library that accelerates inference performance on the latest LLMs on NVIDIA GPUs. NeMo, an end-to-end framework for building, customizing, and deploying generative AI applications, uses TensorRT-LLM and NVIDIA Triton Inference Server for generative AI deployments.