ScaleLLM: An Efficient LLM Inference Solution
ScaleLLM is an innovative inference system crafted to support large language models, commonly referred to as LLMs. This solution is tailored to meet the rigorous demands of production environments, providing support for a wide array of popular open-source models such as Llama3.1, Gemma2, Bloom, and GPT-NeoX, among others.
Development and Availability
ScaleLLM is currently in a phase of active development, with a strong focus on enhancing efficiency and incorporating additional features. The project's roadmap is publicly available for those who wish to track its progress. It’s important to note that the project is also downloadable from PyPI, which makes installation easy using the pip command.
Recent Highlights
- As of June 2024, ScaleLLM has been made available on PyPI, facilitating straightforward installation.
- In March 2024, ScaleLLM introduced advanced features, offering support for CUDA graph, prefix cache, chunked prefill, and speculative decoding.
- The initial release happened in November 2023, bringing in support for several popular open-source models.
Core Features
ScaleLLM boasts a plethora of features engineered to enhance performance and provide flexibility:
- High Efficiency: By utilizing state-of-the-art techniques such as Flash Attention, Paged Attention, and Continuous Batching, ScaleLLM excels in LLM inference performance.
- Tensor Parallelism: This enables the efficient execution of models, ensuring swift performance across different tasks.
- OpenAI-Compatible API: The REST API server is designed to be compatible with OpenAI, supporting both chat and completion functionalities.
- Seamless Integration: Most popular Huggingface models are supported, alongside the ability to handle safetensors.
- Customizability and Production-Readiness: ScaleLLM allows customization to meet user-specific needs while offering robust monitoring and management features ideal for production environments.
Getting Started
To get started with ScaleLLM, installation is straightforward via pip. A variety of options are available to tailor the installation according to different CUDA and Pytorch versions. Additionally, developers can build ScaleLLM from the source if a wheel package is not available for certain configurations.
Advanced Capabilities
ScaleLLM comes equipped with several advanced features designed to optimize performance:
- CUDA Graph: Helps in reducing the kernel launch overhead, optimizing decoding performance.
- Prefix Cache and Chunked Prefill: These features enhance efficiency during inference by caching intermediate states and managing long user prompts.
- Speculative Decoding: A technique to accelerate LLM inference without altering output distribution.
- Quantization: Supports techniques such as GPTQ and AWQ to reduce model memory footprints.
Supported Models
ScaleLLM supports a comprehensive list of models including Aquila, Bloom, Baichuan, and many others, each benefiting from tensor parallelism, quantization, and API support. If a particular model is not supported yet, users are encouraged to request its addition.
Contribution and Acknowledgements
The project is open for contributions, and discussions are welcomed on GitHub and Discord. The community’s input is invaluable for the continued improvement of ScaleLLM.
License
ScaleLLM is distributed under the Apache 2.0 license, allowing users to freely use and modify the codebase within the bounds of this license agreement.
Explore the potential of ScaleLLM today and contribute to its evolution as a leader in efficient LLM inference solutions!