Building an LLM Inference Engine with C++ and CUDA
Building an LLM inference engine from scratch using C++ and CUDA focuses on optimizing throughput for single-GPU inference without relying on external libraries. The approach emphasizes understanding the full stack of LLM inference, covering model loading, token throughput improvements, and utilizing a specific architecture (Mistral v0.2). Key topics include the architecture of large language models, the inference process, CPU and GPU optimizations, and the challenges of memory bandwidth. The final implementation aims to achieve competitive throughput compared to existing solutions while exploring further enhancements and optimizations.
- The project is aimed at creating a custom LLM inference engine using C++ and CUDA.
- It covers key concepts such as model architecture, inference mechanics, and optimization techniques.
- The implementation focuses on improving token throughput beyond existing benchmarks.
- Memory bandwidth and efficient computation are crucial for performance.
- Future work includes further optimizations and exploring the use of libraries for enhanced efficiency.
What is the main goal of the project described in the text?
The primary goal is to build an LLM inference engine from scratch using C++ and CUDA, focusing on optimizing throughput for single-GPU inference.
Which architecture is primarily discussed in the implementation?
The implementation primarily focuses on the Mistral v0.2 architecture.
What are some key optimization techniques mentioned?
Key optimization techniques include memory bandwidth improvements, multithreading, vectorization, and kernel fusing on the CPU and GPU.