Hybrid JIT-CUDA Graph Optimization for Low-Latency Large Language Model Inference
📰 ArXiv cs.AI
Optimize large language model inference with a hybrid JIT-CUDA graph approach to reduce latency and improve performance
Action Steps
- Build a hybrid runtime framework using JIT compilation and CUDA Graph execution
- Configure the framework to optimize kernel launch overhead
- Test the framework with large language models to measure latency reduction
- Apply the optimized framework to interactive and short-sequence settings
- Compare the performance of the hybrid framework with traditional approaches
Who Needs to Know This
ML engineers and researchers working on large language models can benefit from this approach to improve inference speed and reduce kernel launch overhead
Key Insight
💡 Combining JIT compilation with CUDA Graph execution can significantly reduce kernel launch overhead and improve inference speed
Share This
🚀 Reduce LLM inference latency with hybrid JIT-CUDA graph optimization! 📊
Key Takeaways
Optimize large language model inference with a hybrid JIT-CUDA graph approach to reduce latency and improve performance
Full Article
Title: Hybrid JIT-CUDA Graph Optimization for Low-Latency Large Language Model Inference
Abstract:
arXiv:2604.23467v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance across natural language and multimodal tasks, yet their practical deployment remains constrained by inference latency and kernel launch overhead, particularly in interactive, short-sequence settings. This paper presents a hybrid runtime framework that combines Just-In-Time (JIT) compilation with CUDA Graph execution to reduce launch overhead while preserving runtime flexibility during
Abstract:
arXiv:2604.23467v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance across natural language and multimodal tasks, yet their practical deployment remains constrained by inference latency and kernel launch overhead, particularly in interactive, short-sequence settings. This paper presents a hybrid runtime framework that combines Just-In-Time (JIT) compilation with CUDA Graph execution to reduce launch overhead while preserving runtime flexibility during
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