The article discusses the challenges and components required to scale Retrieval Augmented Generation (RAG) from a Proof of Concept (POC) to production. It covers key issues such as performance, data management, risk, integration into workflows, and cost. It also outlines architectural components such as scalable vector databases, caching mechanisms, advanced search techniques, responsible AI layers, and API gateways needed for overcoming these challenges.
This repository contains scripts for benchmarking the performance of large language models (LLMs) served using vLLM.
A startup called Backprop has demonstrated that a single Nvidia RTX 3090 GPU, released in 2020, can handle serving a modest large language model (LLM) like Llama 3.1 8B to over 100 concurrent users with acceptable throughput. This suggests that expensive enterprise GPUs may not be necessary for scaling LLMs to a few thousand users.
A study investigating whether format restrictions like JSON or XML impact the performance of large language models (LLMs) in tasks like reasoning and domain knowledge comprehension.
Explores the performance benefits of running Python without the Global Interpreter Lock (GIL), using the new experimental Python 3.13.0b4 pre-release with the --disable-gil flag. Discusses how this change can lead to faster execution times for CPU-intensive tasks in data science and machine learning.
A discussion post on Reddit's LocalLLaMA subreddit about logging the output of running models and monitoring performance, specifically for debugging errors, warnings, and performance analysis. The post also mentions the need for flags to output logs as flat files, GPU metrics (GPU utilization, RAM usage, TensorCore usage, etc.) for troubleshooting and analytics.
Improving the memory and computational efficiency of Large Language Models (LLMs) for handling long input sequences, including retrieval augmented questions answering, summarization, and chat tasks. It covers various techniques, such as lower precision computing, Flash Attention algorithm, positional embedding methods, and key-value caching strategies. These methods help reduce memory consumption and increase inference speeds while maintaining high accuracy levels in LLM applications. Furthermore, it highlights some advanced approaches like Multi-Query-Attention (MQA) and Grouped-Query-Attention (GQA), which further enhance computational and memory efficiency without compromising performance.
