SGLang vs. vLLM: Inference Runtimes and RadixAttention
Se a primeira fase da inteligência artificial foi dominada pela expansão exponencial de parâmetros de treinamento, o ano de 2026 consolidou uma mudança irreversível de prioridades: a verdadeira guerra da computação neural moderna é travada no runtime de inferência. Com a explosão de agentes autônomos, chamadas repetidas de ferramentas e esteiras de raciocínio de múltiplos passos, o custo e a…
The article discusses the current state of artificial intelligence, specifically the shift in priorities from expanding training parameters to optimizing inference runtime. In 2026, the efficiency of video memory management (VRAM) has become crucial for AI products, driven by the proliferation of autonomous agents and complex workflows.
Two technologies, vLLM's PagedAttention and SGLang's RadixAttention, are competing to optimize memory usage. RadixAttention uses a radix tree data structure to enable instant reuse of shared prefixes, while PagedAttention relies on paginated memory blocks. The article also touches on the impact of speculative decoding on throughput and latency.
Written by urgent.news from Dev.to's report — not a translation of it. Machine-written — may contain errors; check the original before relying on it.
