TensorRT-LLM has demonstrated significant performance improvements in benchmarks, including a 4.6x acceleration in inference performance for Meta's Llama 2 model compared to A100 GPUs. The software uses tensor parallelism to enable efficient inference at scale and includes fully optimized versions of many widely used LLMs. It also introduces an optimized scheduling technique called in-flight batching to manage dynamic loads and improve GPU usage. The software will soon be integrated into the NVIDIA NeMo framework and is currently available in early access.
Key takeaways:
- NVIDIA has developed TensorRT-LLM, an open-source software designed to optimize and accelerate large language model (LLM) inference, which will be released in the coming weeks.
- TensorRT-LLM has been integrated with innovations from leading companies and offers peak performance and quick customization capabilities for new LLMs without requiring deep knowledge of C++ or NVIDIA CUDA.
- The software includes an optimized scheduling technique called in-flight batching, which improves GPU usage and doubles the throughput on a benchmark of real-world LLM requests on H100 Tensor Core GPUs.
- NVIDIA TensorRT-LLM is now available in early access and will soon be integrated into the NVIDIA NeMo framework, part of NVIDIA AI Enterprise.