#LLM Inference

FasterTransformer offers highly optimized transformer-based encoders and decoders for GPU-driven inference. Utilizing CUDA and C++, it integrates seamlessly with TensorFlow, PyTorch, and Triton, providing practical examples. Key features include FP16 precision and INT8 quantization for substantial speedup in BERT, decoder, and GPT tasks, enhancing processing efficiency across NVIDIA GPU architectures.

Explore a comprehensive library of leading-edge papers and code focused on large language model (LLM) inference. It includes topics such as distributed computing, caching methods, and quantization techniques like FP8 and WINT8/4. Designed for researchers and practitioners aiming to improve the efficiency of LLM deployment. The resource covers subjects like Continuous Batching and Parallel Decoding, alongside advancements such as Mooncake and LLM-Viewer. Stay informed on the latest innovations to support your LLM applications.

Discover a user-friendly Python application utilizing the GPT-4 API for effective LLM interactions. This app functions as a guide for developing LLM inference web applications, easily modified for different APIs or models. Detailed instructions for cloning, installing dependencies, and running the app are provided, simplifying the setup process. A comprehensive tutorial is available in Taipy's documentation to aid customization. An active OpenAI API key is necessary for operation.

DashInfer is an optimized C++ runtime that ensures scalable and efficient inference for large language models (LLMs) across multiple hardware platforms, including x86 and ARMv9. It features Continuous Batching and NUMA-Aware support for enhanced CPU performance and minimal third-party dependencies for easy integration. High precision with GPU-level accuracy and support for open-source LLMs like Qwen and LLaMA make it a robust choice. Techniques like Post Training Quantization and Flash Attention further boost performance, ensuring low latency and high throughput in multi-node server setups.