New Techniques to Accelerate Language Models on NVIDIA GPUs | A compact guide to large language models pdf 2021 | Llm meaning tech | Large language models tutorial | Turtles AI

 Apple and NVIDIA collaborate to accelerate the inference of large language models (LLMs) through the ReDrafter technique. This innovation significantly improves the speed of token generation, reducing energy and computational costs for NVIDIA GPU-based applications.

Key points:

• Technical innovation: ReDrafter uses a combination of beam search and attention via tree to optimize the efficiency of text generation.
• Industry collaboration: Apple and NVIDIA worked together to integrate this technology into the NVIDIA TensorRT-LLM framework.
• Superior performance: Benchmarks on NVIDIA GPUs show a 2.7-fold speedup in greedy decoding for models of tens of billions of parameters.
• Practical impact: ReDrafter enables faster production applications, reducing latency and power consumption.

Apple and NVIDIA recently collaborated to optimize the inference performance of large language models (LLMs) by introducing a new technique called ReDrafter, designed to accelerate text generation. This solution, based on a recurrent pattern (RNN), combines two advanced methodologies: beam search and attention by tree, with the goal of improving speed and efficiency in token generation. This approach has been shown in tests to achieve up to 3.5 token generation per generation step, clearly outperforming previous speculative decoding techniques.

The real breakthrough of ReDrafter, however, lies in its practical application. Through close collaboration, Apple and NVIDIA have integrated this technology into NVIDIA’s TensorRT-LLM framework, an acceleration system designed to support open source language models on NVIDIA GPUs. Although TensorRT-LLM already included innovative methods such as Medusa, ReDrafter’s algorithms required the introduction of new operators and optimization of existing ones. These interventions expanded the capabilities of TensorRT-LLM, making it compatible with increasingly sophisticated models and advanced decoding methodologies.

The impact of the technology is reflected in benchmark results: testing a model of tens of billions of parameters on NVIDIA GPUs, the integration of ReDrafter resulted in a 2.7-fold increase in token generation speed over traditional greedy decoding methods. These numbers clearly indicate the potential to reduce latency as perceived by end users, while optimizing operational costs through lower power consumption and the use of fewer hardware resources.

Although this collaboration shows a rare convergence between two historically distant technology giants, it is unlikely to develop into a long-term partnership. However, the success of this initiative could pave the way for further limited and strategic collaborations between Apple and NVIDIA, especially in critical areas such as lAI and machine learning.

This innovation is a significant step forward to improve the efficiency of LLMs and promote the evolution of AI-based manufacturing applications.