NVIDIA/TensorRT-LLM

Features

• GPU-Accelerated - Provides a high-performance deployment platform for serving optimized language models with advanced batching.
• Model Compilation - Transforms machine learning models into highly efficient execution graphs for accelerated text generation.
• Large Language Model Optimization - Compiles and refines machine learning models for specific hardware to maximize throughput and reduce latency.
• Model Optimization - Compiles and refines machine learning models for specific hardware to maximize processing throughput and reduce latency.
• PagedAttention Memory Management - Allocates non-contiguous memory blocks for key-value caches to eliminate fragmentation and support long sequence lengths.
• Model Compilers - Transforms high-level neural network definitions into hardware-specific execution kernels to maximize throughput.
• High-Throughput Model Serving - Deploys optimized models as scalable services to handle concurrent user requests while maintaining low latency.
• Online Model Servers - Deploys optimized models as scalable services, handling concurrent user requests with advanced batching and scheduling.
• Attention Backends - Allocates and retains memory for attention mechanisms to support long sequence processing and data reuse.
• Long-Context Sequence Processors - Allocates and retains memory for attention mechanisms to support processing long sequences and data reuse.
• Inference Batching Schedulers - Groups multiple concurrent inference requests into a single execution pass to optimize hardware utilization.
• Offline Inference Engines - Provides offline batch inference to process large volumes of data through optimized models in non-interactive environments.
• AI & Machine Learning - Easy-to-use API for defining and optimizing LLMs
• Inference and Deployment - High-performance inference engine optimized for NVIDIA GPUs.
• Inference and Deployment Acceleration - NVIDIA's high-performance toolkit for accelerating LLM inference on GPUs.
• Inference and Serving - API for building high-performance inference engines.
• Inference Engines - Optimized inference library for NVIDIA GPU hardware.
• Inference Frameworks - Optimized inference engine for NVIDIA GPU hardware.
• Inference Serving Engines - High-performance inference engine with advanced batch management.
• Machine Learning Libraries - Optimized Python API for LLM inference.
• Model Serving and Inference - Accelerator for high-performance LLM inference and training.
• Model Serving Engines - Inference engine optimized for NVIDIA GPU architectures.
• Static Graph Execution - Pre-calculates computational flow and memory requirements before runtime to ensure predictable performance.
• Kernel Fusion Operations - Combines multiple sequential mathematical operations into single optimized GPU instructions to reduce memory bandwidth overhead.
• Batch Inference Engines - Processes large volumes of data through optimized models in non-interactive environments to maximize hardware utilization.
• Custom Decoding Strategies - Implements custom sampling strategies directly within the execution pipeline to minimize data transfer overhead.
• Generation Controls - Adjusts sampling strategies and decoding logic to manage generated text quality and inference speed.
• Inference Configuration Parameters - Provides configuration settings to adjust sampling strategies and logic for controlling generated text quality.

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