LMDeploy
FreeA high-throughput and low-latency inference and serving framework for LLMs and VLs
FreeFree tier
About LMDeploy
LMDeploy is a high-throughput, low-latency inference and serving framework for large language models (LLMs) and vision-language models (VLMs). It provides a comprehensive toolkit for compressing, deploying, and serving models, supporting advanced features like quantization (int8/int4 KV cache, 4-bit weight-only), TurboMind and PyTorch engines, PD disaggregation, CUDA graphs, and integration with platforms like Huawei Ascend. It supports a wide range of models including Llama, DeepSeek, InternVL, Qwen, and more, and is actively maintained by the InternLM team.
Key Features
High-throughput and low-latency inference
Model compression and quantization (int8/int4 KV cache, 4-bit weight-only)
TurboMind inference engine
PyTorch inference engine with CUDA graph support
Support for multiple GPU platforms (NVIDIA, Huawei Ascend)
PD disaggregation for DeepSeek models
Integration with FlashMLA, DeepGemm, DeepEP for DeepSeek optimization
Offline inference pipeline
Vision-language model support (InternVL, LLaVA, etc.)
Online quantization and inference
Pros & Cons
Pros
- High throughput and low latency compared to alternatives like vLLM
- Active development and support for latest models (DeepSeek V3, Llama3.1)
- Comprehensive compression toolkit (quantization, KV cache optimization)
- Supports both NVIDIA and Huawei Ascend platforms
Cons
- Primarily optimized for NVIDIA GPUs, with limited support for other vendors
- Documentation may require familiarity with LLM deployment frameworks
Best For
Deploying large language models in productionServing vision-language modelsModel compression and quantization for edge deploymentRunning efficient inference on NVIDIA GPUs and Ascend hardware