Distributed LLM Inference System: An Advanced Operating Systems Course Project for Large-Scale Deployment

This article introduces a distributed large language model (LLM) inference system project from an advanced operating systems course, aiming to explore architectural design and system optimization methods for efficient LLM inference in multi-node environments. The project covers key technologies such as model parallelism, pipeline parallelism, and load balancing, which not only address the industrial needs for large-scale LLM deployment but also provide students with opportunities for system engineering training that combines theory and practice.

With the exponential growth of large language model scales, single-machine inference can no longer meet production environment requirements, making distributed LLM inference systems a hot topic of common concern in academia and industry. Originating from an advanced operating systems course, this project explores core challenges and solutions for LLM inference in distributed environments through practice, and has important academic research and engineering practice value.

Large-scale LLM deployment faces multiple technical challenges:

1. Balancing model parallelism and pipeline parallelism while minimizing communication overhead;
2. Optimizing communication bottlenecks, including improving the efficiency of collective communication operations like All-Reduce;
3. Load balancing and elastic scaling to handle request fluctuations;
4. Fault tolerance and high availability to ensure single-point failures do not affect services.

The system architecture design includes:

1. Master-slave architecture and coordination mechanism: the master node is responsible for scheduling and state management, while worker nodes perform inference;
2. Request routing and batching strategy to balance throughput and latency;
3. Memory management and KV cache distribution to address cross-node cache transmission bottlenecks.

Technical implementation and optimization directions include:

1. Network communication optimization, such as RDMA, GPUDirect RDMA, and custom protocols;
2. Innovation in scheduling algorithms, including reinforcement learning-based adaptive scheduling and affinity scheduling;
3. Support for heterogeneous computing, which senses hardware differences to achieve optimal task allocation.

As a course project, its educational value is reflected in:

1. Combining theory and practice, applying operating system theoretical knowledge;
2. Cultivating system engineering capabilities, covering areas such as network programming and concurrency control;
3. Accumulating performance tuning experience, and gaining an in-depth understanding of bottleneck analysis and optimization methods.

The directions for further development of the project include: supporting more model architectures and parallel strategies; improving monitoring and observability; exploring distributed inference under Serverless architecture; and researching edge-cloud collaborative inference modes.