Tag: LLMInference

vLLM Features

Posted on by lechuck park

vLLM Features & Architecture Breakdown

This chart outlines the key components of vLLM (Virtual Large Language Model), a library designed to optimize the inference speed and memory efficiency of Large Language Models (LLMs).

1. Core Algorithm

  • PagedAttention
    • Concept: Applies the operating system’s (OS) virtual memory paging mechanism to the attention mechanism.
    • Benefit: It resolves memory fragmentation and enables the storage of the KV (Key-Value) cache in non-contiguous memory spaces, significantly reducing memory waste.

2. Data Unit

  • Block (Page)
    • Concept: The minimum KV cache unit with a fixed token size (e.g., 16 tokens).
    • Benefit: Increases management efficiency via fixed-size allocation and minimizes wasted space (internal fragmentation) within slots.
  • Block Table
    • Concept: A mapping table that connects Logical Blocks to Physical Blocks.
    • Benefit: Allows non-contiguous physical memory to be processed as if it were a continuous context.

3. Operation

  • Pre-allocation (Profiling)
    • Concept: Reserves the maximum required VRAM at startup by running a dummy simulation.
    • Benefit: Eliminates the overhead of runtime memory allocation/deallocation and prevents Out Of Memory (OOM) errors at the source.

4. Memory Handling

  • Swapping
    • Concept: Offloads data to CPU RAM when GPU memory becomes full.
    • Benefit: Handles traffic bursts without server downtime and preserves the context of suspended (waiting) requests.
  • Recomputation
    • Concept: Recalculates data instead of swapping it when recalculation is more cost-effective.
    • Benefit: Optimizes performance for short prompts or in environments with slow interconnects (e.g., PCIe limits).

5. Scheduling

  • Continuous Batching
    • Concept: Iteration-level scheduling that fills idle slots immediately without waiting for other requests to finish.
    • Benefit: Eliminates GPU idle time and maximizes overall throughput.

Summary

  1. vLLM adapts OS memory management techniques (like Paging and Swapping) to optimize LLM serving, solving critical memory fragmentation issues.
  2. Key technologies like PagedAttention and Continuous Batching minimize memory waste and eliminate GPU idle time to maximize throughput.
  3. This architecture ensures high performance and stability by preventing memory crashes (OOM) and efficiently handling traffic bursts.

#vLLM #LLMInference #PagedAttention #AIArchitecture #GPUOptimization #MachineLearning #SystemDesign #AIInfrastructure

With Gemini