vllm-project/vllm

Features

• Distributed Model Servers - Exposes generative model capabilities through standard network protocols for integration into external applications and chat interfaces.
• High-Throughput Model Serving - Scales large language model inference to handle high volumes of concurrent requests with minimal latency.
• Online Model Servers - Hosts an online server that provides real-time completions and chat responses via standard API protocols.
• Local Inference Engines - Enables execution of advanced generative models directly on local hardware for private and low-latency inference.
• Custom Model Execution Engines - Executes custom model architectures using highly optimized native implementations and support for various data formats.
• Inference Engines - Maximizes token generation speed and memory efficiency when serving large language models to multiple concurrent users.
• Continuous Batching Strategies - Dynamically inserts new sequences into active inference batches to maximize hardware utilization.
• Model Quantization Frameworks - Compresses large neural networks to reduce memory footprint while maintaining performance on resource-constrained hardware.
• Hardware-Accelerated Compute Backends - Maps complex mathematical operations onto diverse graphics processing units and specialized silicon using optimized kernels.
• PagedAttention Memory Management - Manages key-value cache memory in non-contiguous blocks to eliminate fragmentation and enable efficient dynamic batching.
• Request Schedulers - Decouples request ingestion from the inference loop to prioritize incoming traffic for high concurrency and low latency.
• Offline Inference Engines - Processes large language model inference in batch mode with support for custom sampling and generation parameters.
• Model Downloaders - Facilitates the retrieval and loading of model weights and configuration files from remote storage for immediate execution.
• Attention Backends - Supports configurable, high-performance attention backends that automatically detect and optimize computation for specific hardware accelerators.
• Model Quantization - Reduces memory footprint and computational requirements to enable deployment of massive neural networks on resource-constrained hardware.
• Cross-Platform AI Accelerators - Optimizes generative model performance across diverse hardware architectures, including specialized GPUs and consumer-grade silicon.
• AI and Agents - A high-throughput and memory-efficient inference and serving engine for LLMs.
• Inference and Deployment - High-throughput serving engine utilizing PagedAttention for faster inference.
• Inference and Deployment Acceleration - High-throughput serving engine utilizing PagedAttention for efficient model inference.
• Inference and Serving - High-throughput engine for model serving.
• Inference Engines - High-throughput and memory-efficient engine for serving LLMs.
• Inference Frameworks - Memory-efficient serving using paged attention mechanisms.
• KV Cache Management - Paged attention for efficient memory management in serving.
• Large Language Models - High-throughput serving engine for LLM inference.
• Machine Learning Operations - High-throughput and memory-efficient inference library for LLMs.
• Model Serving - High-throughput and memory-efficient LLM inference engine.
• Model Serving & Deployment - Provides a high-throughput, memory-efficient LLM serving engine.
• Model Serving Engines - High-throughput, memory-efficient inference engine for LLMs.
• Model Deployment - Listed in the “Model Deployment” section of the Llm Course awesome list.
• Inference Frameworks - High-throughput inference engine with PagedAttention.
• Large Language Models (LLMs) - Listed in the “Large Language Models (LLMs)” section of the The Incredible Pytorch awesome list.
• Compute Backends - Dispatches computational tasks to specialized hardware backends while maintaining consistent high-level model execution logic.
• Kernel Fusion Strategies - Combines multiple operations into single GPU kernels to reduce memory overhead and improve computational throughput.
• Custom Model Architectures - Integrates and serves specialized or proprietary model architectures within a standardized production environment for consistent inference results.
• Apple Silicon Accelerators - Accelerates model execution on Apple M-series hardware by providing specialized packages that optimize for native graphics processing.

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