The emergence of relatively smaller models like Alpaca, BloomZ, and Vicuna open a new opportunity for enterprises to lower the cost of fine-tuning and inference in production. As demonstrated above, high-quality quantization brings high-quality chat experiences to Intel CPU platforms...
每个GPU与集群中的所有其他GPU都连接在一起,使用Mellanox InfiniBand等高带宽互连,形成高带宽数据平面。目前云中提供的InfiniBand带宽范围从每个GPU的25GBps到每个GPU的50GBps [5],[8]。默认的优化GPU间通信库是NCCL,用于通过NVLink或InfiniBand在GPU之间进行通信[13]。 III. CHARACTERIZATION 在本节中,我们探讨了LLM...
Ramya Ravi, AI Software Marketing Engineer, Intel | LinkedIn Large neural network models are the essence of many recent advances in AI and deep learning applications. Large language models (LLMs) belong to the class of deep learning models that can mimic hum...
to accelerate the LLM inference on CPUs. We demonstrate the general applicability of our approach on popular LLMs including Llama2, Llama, GPT-NeoX, and showcase the extreme inference efficiency on CPUs. The code is publicly available at: https://github.com/intel/intel-extension-for-...
Intel no longer accepts patches to this project. Please refer tohttps://github.com/intel/intel-extension-for-pytorchas an alternative Neural Speed Neural Speed is an innovative library designed to support the efficient inference of large language models (LLMs) on Intel platforms through the state...
Welcome to libLLM, an open-source project designed for efficient inference of large language models (LLM) on ordinary personal computers and mobile devices. The core is implemented in C++14, without any third-party dependencies (such as BLAS or SentencePiece), enabling seamless operation across a...
DRAM, with a 4-5x and 20-25x increase in inference speed compared to naive loading approaches in CPU and GPU, respectively. 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 ...
LLM inference较慢的主要原因,一是其网络本身的复杂性,二是其auto-regression decoding,tokens must be decoded one by one。因此,目前对基于Transformer的LLM Inference Acceleration方法主要有以下几个方向: 1.模型压缩 具体方向包括Knowledge Distillation、Quantization、Pruning、Sparsity、Mixture-of-Experts。这类方法的...
Efficient Inference Application Optimization Efficient Training, Quantum ML 04:15什么是Efficient AI 随着AI的发展,算力的供给已经不满足需求。绿线表示GPU显存的增速,红线表示模型参数的增速。 为了解决供给小于需求的问题,Pruning, Sparsity and Quantization 的方式被提出, 来压缩模型的参数大小 ...
This work is in continuation to ourprevious work, where we performed an inferencing experiment on Llama2 7B and shared results on GPU performance during the process. Memory bottleneck When finetuning any LLM, it is important to understand the infrastructure needed to load and fine-tune...