SGLang vs. vLLM: Runtimes de Inferência e RadixAttention

In a world where the first era of artificial intelligence was defined by the exponential expansion of training parameters, the year 2026 marked a seismic shift in the priorities of modern neural computing. As autonomous agents proliferated, repeated calls to tools and multi-step reasoning chains transformed the cost and feasibility of AI products from being solely determined by raw GPU quantities in data centers to being dictated by the algorithmic efficiency of video memory (VRAM) management.

For years, vLLM had set the market standard using the revolutionary PagedAttention algorithm, which eliminated memory fragmentation by treating the Key-Value Cache (KV Cache) as virtual memory pages akin to an operating system. However, the proliferation of highly dynamic workflows—where agents shared massive system prompts, performed tensor tree search (MCTS) trees, and invoked tools repeatedly—exposed the limitations of static block paging.

It was in this landscape that SGLang emerged as the leading high-performance competitor, introducing the concept of RadixAttention: a radix tree-based data structure that indexed the KV Cache hierarchically across different requests, enabling instant re-use of shared prefixes with zero recomputation cost. Meanwhile, the maturation of Speculative Decoding, particularly with variants like EAGLE 3.1 and Medusa, transformed both runtimes into throughput monsters.

Instead of calculating a single token per self-recursive step (the classic memory bottleneck where the GPU spent 90% of its time transferring weights between HBM and tensor cores), modern servers utilized lightweight draft models to generate multiple token candidates simultaneously, verifying them in parallel with a single pass of the main model's attention.

This detailed technical dossier from PromptX dissected the fundamental engineering behind the SGLang versus vLLM showdown in 2026, analyzing the mathematics behind RadixAttention and PagedAttention v3, comparing real-world throughput and latency numbers on industrial clusters (NVIDIA H100 SXM5 and H200), exploring the operational economics of Chunked Prefill, and delivering a complete and asynchronous Python harness (formatted in continuous simple spacing PEP 8, 1.0x) for measuring Time-To-First-Token (TTFT) and benchmarking under massive concurrency.