This post discusses the most pressing challenges in LLM inference, along with some practical solutions. Readers should have a basic understanding oftransformer architectureand the attention mechanism in general.
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In contrast, performance benchmarking, as demonstrated by the NVIDIA GenAI-Perf tool, is concerned with measuring the actual performance of the model itself, such as its throughput, latency, and token-level metrics. This type of testing helps identify issues related to model efficiency, optimizatio...
TensorRT-LLM 还为NVIDIA NeMo提供支持,后者为开发人员提供了端到端的云原生企业框架,用于构建、自定义和部署具有数十亿个参数的生成式 AI 模型。立即开始使用 NeMo。 源文:Mastering LLM Techniques: Inference Optimization | NVIDIA Technical Blog 发布于 2025-03-23 22:40・北京 推理 LLM 大模型优化 ...
For developers working with LLMs, Intel’s article serves as a practical guide to navigating the complexities of fine-tuning and inference, offering valuable insights and techniques for optimizing both the development and deployment phases.
In depth optimizations: Standard inference optimization techniques are important (eg. operator fusion, weight quantization) for LLMs but it's important to explore deeper systems optimizations, especially those which improve memory utilization. One example is KV cache quantization. Hardware configurations: ...
[6]1506.02626.pdf (arxiv.org) [7]Weeknotes: Fine-pruning transformers, universal data augmentation [8]https://lilianweng.github.io/posts/2023-01-10-inference-optimization/ 浮天水送无穷树,带雨云埋一半山。 —— 辛弃疾《鹧鸪天·送人》
4 Inference 首先请参考2.2 Model Architecture中,关于基本推理过程,KV Cache,GQA部分的内容,同时请参考3.2 SFT中关于PagedAttention的介绍。 4.1 Parallelism Parallelism,LLM分布式训练推理的一部分,包括Data Parallelism和Model Parallelism,本节做一些介绍。同样涉及到OS的一些概念。
Context Optimization:如果模型没有相应的知识,比如一些私有数据。 LLM Optimization:如果模型不能产生正确的输出,比如不够准确或者不能遵循指令按照特定的格式或风格输出。 在实践中,通常是利用各种技术不断地迭代来达到生产部署的需求,很多时候这些技术是可以累加的,需要找到有效的方法将这些改进组合起来,以获得最佳效果。
The researchers introduce Thought Preference Optimization (TPO), a training method that guides LLMs to learn and optimize their internal thought processes. The idea behind TPO is to train an LLM to create a response consisting of two parts: a “thought” part and a “response” part. The tho...