TinyServe-vLLM: Optimized vLLM with Advanced Kernel Optimizations

TinyServe-vLLM is an enhanced version of vLLM with advanced CUDA kernel optimizations for improved memory management, fragmentation reduction, and query-aware cache selection.

Key TinyServe Optimizations

• Query-Aware Cache Selection: Intelligent cache management based on query characteristics and access patterns
• Fragmentation Reduction: Advanced techniques to minimize memory fragmentation (40-60% reduction)
• FlashAttention Integration: Combined FlashAttention with PagedAttention for maximum efficiency
• Best-Fit Allocation: Optimized block allocation strategies to reduce wasted space by 20-30%
• Memory Utilization: >96% memory utilization (vs 60-80% traditional)

TinyServe Kernel Features

• Fragmentation detection and analysis
• Fragmentation-aware block allocation
• Intelligent defragmentation
• Continuous block allocation (best-fit strategy)
• Dynamic workload balancing
• LRU cache management

TinyServe-vLLM Implementation

We have updated/added the following files to enhance vLLM with TinyServe optimizations:

Core Kernel Files

• csrc/vllm_kernels.cu: Complete CUDA kernel implementation with TinyServe optimizations

  • Fragmentation detection and analysis kernel
  • Fragmentation-aware block allocation kernel
  • Defragmentation kernel
  • Continuous block allocation (best-fit) kernel
  • FlashAttention with PagedAttention integration
  • Advanced block allocation with LRU cache
  • Intelligent memory compaction
  • Dynamic workload balancing

• csrc/tinyserve_kernels.h: Header file for TinyServe kernels

  • Complete API definitions for all TinyServe optimization kernels
  • Data structures for block table management
  • Function declarations for kernel launchers

• csrc/tinyserve_example.cu: Example implementation demonstrating TinyServe usage

  • Complete example showing how to use TinyServe kernels
  • Performance benchmarking utilities
  • Memory compaction examples

Key Features Implemented

1. Query-Aware Cache Selection

   • Query complexity analysis kernel
   • Cache selection strategy kernel
   • Adaptive cache management kernel
   • Integration with PagedAttention

2. Fragmentation Reduction

   • Fragmentation analysis: Detects memory fragmentation patterns
   • Fragmentation-aware allocation: Best-fit strategy to minimize fragmentation
   • Defragmentation: Moves blocks to consolidate free space
   • Continuous block allocation: Allocates consecutive blocks efficiently

3. Memory Management Optimizations

   • LRU cache management for block allocation
   • Access pattern learning and optimization
   • Intelligent memory compaction based on block weights
   • Dynamic workload balancing across GPU warps

For detailed information about TinyServe optimizations, see the TinyServe Kernels Header.

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Citation

If you use TinyServe-vLLM for your research, please cite our paper:

@inproceedings{liu2025tinyserve,
  title={TinyServe: Query-Aware Cache Selection for Efficient LLM Serving},
  author={Liu, Dong and Yu, Yanxuan},
  booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
  pages={12529--12537},
  year={2025}
}

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About

TinyServe-vLLM is an enhanced version of vLLM with advanced CUDA kernel optimizations for improved memory management, fragmentation reduction, and query-aware cache selection. It is developed Dong Liu and Yanxuan Yu at FastLM.ai

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TinyServe-vLLM is fast with:

• State-of-the-art serving throughput with Query-Aware Cache Selection for intelligent cache management
• Efficient management of attention key and value memory with [PagedAttention] enhanced by Fragmentation Reduction (40-60% reduction)
• Best-Fit Block Allocation strategies that reduce wasted space by 20-30%
• Memory Utilization exceeding 96% (vs 60-80% traditional methods)
• Optimized CUDA kernels with FlashAttention integration and PagedAttention fusion
• Fragmentation-aware allocation that minimizes memory fragmentation
• Intelligent defragmentation for continuous memory optimization
• Dynamic workload balancing across GPU warps for maximum utilization

TinyServe-vLLM is flexible and easy to use with:

• Seamless integration with vLLM's existing infrastructure and popular Hugging Face models
• High-throughput serving with query-aware cache selection that adapts to query characteristics
• Fragmentation detection and analysis for proactive memory management
• Continuous block allocation using best-fit strategy for optimal memory layout
• LRU cache management with access pattern learning for intelligent block placement
• Support for NVIDIA GPUs with optimized CUDA kernels
• Adaptive cache management that continuously improves cache selection over time
• Memory compaction based on block weights and access frequencies

TinyServe-vLLM seamlessly supports all models compatible with vLLM, including:

• Transformer-like LLMs (e.g., Llama)
• Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
• Embedding Models (e.g., E5-Mistral)
• Multi-modal LLMs (e.g., LLaVA)

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