vLLM V1 Implementation 7: Internal Structure of GPUModelRunner and Inference Execution
Published:Dec 28, 2025 03:00
•1 min read
•Zenn LLM
Analysis
This article from Zenn LLM delves into the ModelRunner component within the vLLM framework, specifically focusing on its role in inference execution. It follows a previous discussion on KVCacheManager, highlighting the importance of GPU memory management. The ModelRunner acts as a crucial bridge, translating inference plans from the Scheduler into physical GPU kernel executions. It manages model loading, input tensor construction, and the forward computation process. The article emphasizes the ModelRunner's control over KV cache operations and other critical aspects of the inference pipeline, making it a key component for efficient LLM inference.
Key Takeaways
- •ModelRunner is a core component for executing inference in vLLM.
- •It translates inference plans into GPU kernel executions.
- •It manages model loading, input tensor construction, and forward computation.
Reference
“ModelRunner receives the inference plan (SchedulerOutput) determined by the Scheduler and converts it into the execution of physical GPU kernels.”