With an efficient API tool powered by NVIDIA GPUs and optimized for fast inference with NVIDIA® TensorRT™-LLM, Perplexity makes it easy for developers to integrate cutting-edge, open-source large language models (LLMs) into their projects.
Inference with Reference: Lossless Acceleration of Large Language Models(论文详解)——一种大模型加速的方式 使用场景 在许多应用场景中,大模型的输出常常与一些参考文本有很大的相似性,例如在以下三个常见的场景中: 检索增强的生成。New Bing 等检索应用在响应用户输入的内容时,会先返回一些与用户输入相关的信息,...
现有的系统在GPU内存管理方面效率不高,无法充分利用硬件资源,特别是在处理大型模型时。 4.分布式推理的需求:随着模型规模的增长,单个GPU已无法容纳整个模型,需要跨多个GPU进行分布式推理。这要求服务系统能够支持模型并行和流水线并行技术。 机遇 1.抢占: - 现有的LLM推理服务系统通常采用先来先服务(FCFS)的调度策略,...
Our integration of sparsity awareness, context-adaptive loading, and a hardware-oriented design paves the way for effective inference of LLMs on devices with limited memory. Figure 1: LLM in a flash. Related readings and updates. Cut Your Losses in Large-Vocabulary Language Models As language ...
We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real...
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models Large language models (LLMs) have been applied in various applications due to their astonishing capabilities. With advancements in technologies such as chain-of-thought (CoT) prompting and in-conte...
Overall, the cost of hosting a large language model can be significant and require careful planning and budgeting. However, the benefits of using these models for natural language processing tasks can outweigh the costs in many cases. When it comes to performance of a large languag...
LANGUAGE modelsFLEXIBLE work arrangementsPHYSIOLOGICAL effects of accelerationAUTOREGRESSIVE modelsPREDICTION modelsDEEP learningBINOMIAL distributionAs the size of deep learning models continues to expand, the elongation of inference time has gradually evolved into a significant challenge to efficiency and ...
OpenAI releasedGPT-3, a 175 billion-parameter model that generated text and code with short written prompts.In 2021, NVIDIA and Microsoft developed Megatron-Turing Natural Language Generation 530B, one of the world’s largest models for reading comprehension and natural language inference, with 530...
论文简述:在这篇名为Amortizing intractable inference in large language models的论文中,作者提出了一种新的方法来解决大型语言模型(LLM)在处理难以计算的后验分布时的挑战。这些任务包括序列延续、填充和其他形式的约束生成等。为了解决这个问题,作者采用了基于再分配贝叶斯推理的方法来从这些难以计算的后验分布中进行...