153 Python Module Index 155 Index 157 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management including parallel sampling, beam search, and more - Tensor parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs and AMD GPUs - (Experimental) PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

mheducation.com https://dev.twitter.com ## Today's Agenda • An Overview of Angular 2 • Handling the Offline Status • ServiceWorker API • ServiceWorker and Angular 2 • Redux and Angular 2 ## Angular 2 ## [Image](/uploads/documents/4/1/0/a/410acb7e77c2203d530a437a5094a970/p18_1.jpg) ## Handling Offline Status ## Resilient Apps • Treat offline as the norm • All request must have a fallback • Use available API's to network capabilities - Adapt application logic to match the device & network capabilities ## Offline Statuses  ![Im

tables 59 Python Module Index 61 Index 63 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management including parallel sampling, beam search, and more - Tensor parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs and AMD GPUs - (Experimental) PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. ## DOCUMENTATION ## 1.1 Installation vLLM is

205 Python Module Index 207 Index 209 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management sampling, beam search, and more - Tensor parallelism and pipeline parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs, AMD CPUs and GPUs, PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

205 Python Module Index 207 Index 209 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management sampling, beam search, and more - Tensor parallelism and pipeline parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs, AMD CPUs and GPUs, PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

217 Python Module Index 219 Index 221 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management sampling, beam search, and more - Tensor parallelism and pipeline parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs, AMD CPUs and GPUs, PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

205 Python Module Index 207 Index 209 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management sampling, beam search, and more - Tensor parallelism and pipeline parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs, AMD CPUs and GPUs, PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

183 Python Module Index 185 Index 187 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management sampling, beam search, and more - Tensor parallelism and pipeline parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs, AMD CPUs and GPUs, PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

123 Python Module Index 125 Index 127 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management including parallel sampling, beam search, and more - Tensor parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs and AMD GPUs - (Experimental) PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation

135 Python Module Index 137 Index 139 ## LLM vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management including parallel sampling, beam search, and more - Tensor parallelism support for distributed inference - Streaming outputs - OpenAI-compatible API server - Support NVIDIA GPUs and AMD GPUs - (Experimental) PagedAttention) - vLLM paper (SOSP 2023) - How continuous batching enables 23x throughput in LLM inference while reducing p50 latency by Cade Daniel et al. - vLLM Meetups. ## DOCUMENTATION ## 1.1 Installation