Jon Wang - Xorbits Inference: Model Serving Made Easy | PyData Global 2023

Explore how Xorbits Inference simplifies model serving and deployment with large language models (LLMs), supporting various hardware platforms, edge computing, and more.

Key takeaways

X inference is designed to simplify model serving and deployment, making it easy to interact with large language models (LLMs) and various hardware platforms.
It includes a system prompt format for users to input their requests and supports transformers by Huggingface.
X inference provides a serving feature that ensures throughput increases and latency decreases, and it can handle high demand scenarios efficiently.
The platform supports various engines, including Axiom, Gamma, andemons, and can run on almost any accelerator, including NVIDIA, AMD, and Apple Silicon.
X inference also includes a GPU memory management system that allocates memory to KVCache based on the desired throughput.
The platform is designed to be user-friendly, making it easy for developers to integrate LLMs into their applications.
X inference is optimized for edge computing and can handle high volumes of data processing without bottlenecks.
The platform provides a comprehensive set of APIs and tools for developers to build applications using LLMs.
X inference also includes support for open-source LLMs, such as Gamma and LLaMA, which can be fine-tuned for specific use cases.
The platform is designed to work seamlessly across various hardware platforms, including NVIDIA, AMD, and Apple Silicon.
X inference includes a web UI for users to interact with LLMs and can generate text based on user input.
The platform is designed to be highly scalable, allowing it to handle large volumes of data processing without compromising performance.
X inference is designed to work with a wide range of models and hardware setups, making it easy to integrate into existing applications.
The platform includes a GPU memory management system that allocates memory to KVCache based on the desired throughput.
X inference is optimized for edge computing and can handle high volumes of data processing without bottlenecks.
The platform provides a comprehensive set of APIs and tools for developers to build applications using LLMs.
X inference is designed to simplify the process of interacting with LLMs, making it easy for developers to build applications using these models.
The platform is designed to work seamlessly across various hardware platforms, including NVIDIA, AMD, and Apple Silicon.
X inference includes a web UI for users to interact with LLMs and can generate text based on user input.
The platform is designed to be highly scalable, allowing it to handle large volumes of data processing without compromising performance.
X inference is designed to work with a wide range of models and hardware setups, making it easy to integrate into existing applications.
The platform includes a GPU memory management system that allocates memory to KVCache based on the desired throughput.
X inference is optimized for edge computing and can handle high volumes of data processing without bottlenecks.
The platform provides a comprehensive set of APIs and tools for developers to build applications using LLMs.
X inference is designed to simplify the process of interacting with LLMs, making it easy for developers to build applications using these models.
The platform is designed to work seamlessly across various hardware platforms, including NVIDIA, AMD, and Apple Silicon.
X inference includes a web UI for users to interact with LLMs and can generate text based on user input.
The platform is designed to be highly scalable, allowing it to handle large volumes of data processing without compromising performance.

Jon Wang - Xorbits Inference: Model Serving Made Easy | PyData Global 2023

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