Why vLLM is the King of High-Throughput LLM Serving - By Suyog Kale, CTO, RagnarDataOps скачать в хорошем качестве

Why vLLM is the King of High-Throughput LLM Serving - By Suyog Kale, CTO, RagnarDataOps

In this video, we dive deep into the architectural optimisations that allow vLLM to outperform "naive" Hugging Face and PyTorch serving. While traditional methods often struggle with memory fragmentation and slow generation, vLLM treats LLM serving more like an operating system problem—focusing on advanced memory management and scheduling.Key Topics Covered:• PagedAttention (KV Caching): Discover how vLLM solves the "contiguous tensor" problem. In naive implementations, GPU memory is wasted because the system reserves the maximum sequence length for every request, regardless of actual usage. vLLM treats the KV cache like virtual memory, using fixed-size pages allocated only when needed. This results in compact memory usage, rare OOM (Out-of-Memory) crashes, and the ability to handle massive concurrency.• Speculative Decoding: Learn how vLLM moves beyond the slow "token-by-token" generation. By using a fast draft model alongside a verifier model that share the same KV cache, vLLM can check multiple tokens in a single forward pass. This increases tokens per second and ensures the GPU is fully saturated rather than sitting idle between steps.• MQA & GQA Awareness: Understand how vLLM optimises Multi-Query Attention (MQA) and Grouped-Query Attention (GQA). Unlike traditional Multi-Head Attention (MHA), which stores unique KV data for every head, vLLM allows heads to share KV data. This drastically reduces the KV memory footprint, enabling longer contexts and higher user capacity.Why vLLM Wins: It isn't just one feature—it is the alignment of Paged KV Caching, Speculative Decoding, and MQA-aware attention. Together, these allow for stable latency, massive batching, and significantly lower costs per token. #vLLM #LLM #MachineLearning #AIInfrastructure #GPUServing #DeepLearning #MLOps