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Analysis of 7 tools for serving language model inference
Haebom
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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, provides various decoding algorithms
Cons: Complexity in adding custom models, lack of adapter support
Text Generation Inference
Pros: Native support with HuggingFace, easy setup via Docker
Cons: Lack of adapter support, poor documentation
CTranslate2
Pros: Fast and efficient execution on CPU and GPU, lots of optimizations
Cons: No built-in REST server, lack of adapter support
DeepSpeed-MII
Pros: Load balancing, support for multiple model repositories
Cons: No official release, limited models supported
OpenLLM
Pros: Adapter support, multiple runtime implementations
Cons: Lack of batch support, no distributed inference support
Ray Serve
Pros: Monitoring dashboard, auto-scaling, integration with various libraries.
Cons: Lack of model optimization, high barrier to entry
MLC LLM
Pros: Platform native runtime, memory optimization
Cons: Limited functionality of using LLM model, complex installation
Personally, I use Text Generation Inference. There was no analysis like the above article, but it's just easy to use...
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