InternLM/lmdeploy

Features

• LLM Deployment Frameworks - Provides a framework for distributing large model services across multiple machines using request distribution and tensor parallelism.
• Large Language Model Deployments - Provides the infrastructure and framework necessary to deploy and serve large language models in production environments.
• High Throughput Inference - Maximizes requests processed per second using continuous batching, tensor parallelism, and optimized kernels.
• Inference Optimization Kernels - Uses advanced execution kernels to increase requests per second and process model data more efficiently.
• Kernel Optimizations - Provides specialized low-level CUDA and C++ kernels to accelerate matrix multiplications and attention mechanisms.
• Large Language Model Serving - Hosts and exposes large language and vision models via high-performance inference engines.
• High-Throughput Model Serving - Implements a high-performance runtime designed to handle large volumes of concurrent inference requests with low latency.
• Continuous Batching Strategies - Implements continuous batching strategies to dynamically insert new requests into active inference batches for high hardware utilization.
• Model Quantization - Implements techniques and tools for reducing model memory footprint and computational requirements to improve inference performance.
• Model Servers - Provides a deployment environment capable of running vision language models that process combined image and text inputs.
• Model Serving Interfaces - Provides unified interfaces for deploying and serving multimodal model architectures across diverse hardware environments.
• Vision-Language Models - Runs multimodal vision-language models that process combined image and text inputs across multiple accelerators.
• KV Cache Quantizers - Reduces memory footprint by converting both high-precision floating point weights and KV caches to lower-bit formats.
• Tensor Parallelism - Splits model weights across multiple GPUs to handle larger models and increase throughput via tensor parallelism.
• LLM Inference Optimization - Increases request throughput and reduces latency using tensor parallelism and continuous batching for high-performance serving.
• LLM Production Infrastructure - Provides infrastructure for managing the deployment, scaling, and reliability of large language models in production.
• Batch Inference Pipelines - Allows processing large datasets through a local model for results without requiring an active API server.
• Distributed Model Orchestration - Manages and balances workloads across multiple machines to serve several models simultaneously to a large user base.
• Model Compression Suites - Ships a comprehensive toolkit for reducing model size through weight and cache quantization.
• Distributed Deployment Utilities - Deploys multi-model services across multiple machines using a request distribution system to balance workloads.
• Multimodal Pipeline Coordinators - Coordinates the flow of image and text data through distinct encoders before processing them in a unified transformer.
• Model Compression - Reduces model size and memory requirements using weight and cache quantization to fit models into smaller hardware.
• Weight Quantization - Decreases memory usage and increases speed by applying quantization to model weights and caches.
• Inference Load Balancers - Distributes inference tasks across a cluster of machines to prevent bottlenecks and maximize resource utilization.
• Inference and Serving - Toolkit for compressing and serving large models.
• Inference Engines - Toolkit for compressing and serving models.
• Inference Frameworks - Distributed inference toolkit supporting quantization and multiple API interfaces.
• Model Serving & Deployment - Compresses and deploys LLMs for production.
• High-Performance Serving Systems - Toolkit for compressing and serving LLMs with multi-model support.
• Inference Frameworks - Framework for quantization, inference, and serving of LLMs and VLMs.

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