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Analysis of 7 tools for serving language model inference

Haebom
We compare and analyze seven open source libraries for LLM (Large Language Models) inference and serving. Below is a summary of the main features and pros and cons of each framework.

vLLM

Pros: Fast text generation speed and a range of decoding algorithms
Cons: Adding custom models is complicated, and adapter support is lacking

Text Generation Inference

Pros: Native integration with HuggingFace and easy setup via Docker
Cons: Not enough adapter support and lacking documentation

CTranslate2

Pros: Fast and efficient execution on CPU and GPU, with a variety of optimization features
Cons: Doesn't have a built-in REST server and lacks adapter support

DeepSpeed-MII

Pros: Load balancing and supports various model repositories
Cons: No official releases, limited model support

OpenLLM

Pros: Supports adapters and offers multiple runtime implementations
Cons: No batch support and doesn't support distributed inference

Ray Serve

Pros: Monitoring dashboards, auto-scaling, and easy integration with a variety of libraries
Cons: No model optimization features, and a high barrier to entry

MLC LLM

Pros: Platform-native runtime and good memory optimization
Cons: Limited LLM model functionality and a complicated installation process
Personally, I use Text Generation Inference. I didn't really analyze it like in the post above; it's just easy to use for me...
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