vLLM and SGLang matched or exceeded DeepSeek's custom DeepInfer engine on DeepSeek-V4.1-Flash within days by adopting its open-sourced operators and optimizing for model-specific traits, proving general-purpose inference engines remain dominant due to full production-grade systems.
This comprehensive guide explains inference frameworks for large language models, covering core concepts like Prefill/Decode and KV Cache, comparing major open-source frameworks (vLLM, SGLang, TensorRT-LLM), detailing request processing flow from scheduling to sampling, and identifying who benefits from understanding inference infrastructure.
The article details how 无问芯穹’s Agentic Infra strategy uses a cross‑cluster heterogeneous PD‑separation architecture (PDD) to cut first‑token latency by 51.5% and token cost by 37.5%, explains the bandwidth bottleneck of KV‑Cache transfer, introduces Decode‑side RadixCache and three‑stage handoff mechanisms, and shows a 37.5% BCR improvement that translates into roughly ten‑fold inference cost reduction.
This article analyzes how the xLLM open‑source LLM inference engine tackles the coexistence of multiple priority levels and strict SLO latency targets by introducing a dynamic, SLO‑aware batch scheduler and a PD‑separation architecture that improve throughput and SLO satisfaction across diverse workloads.
The article examines the memory and latency challenges of 1750‑billion‑parameter LLM inference, introduces the xLLM framework’s Slidebatching and PD‑separation scheduling strategies, and details how these techniques achieve up to 35% system‑throughput gains and 52% SLO compliance improvements in real‑world multi‑priority workloads.
This article surveys the 2025 AI infrastructure landscape, covering distributed inference with PD‑separation, dynamic DOPD scheduling, AFD attention‑FFN disaggregation, high‑bandwidth cross‑machine communication libraries, the TileLang programming model, RL train‑inference decoupling via SeamlessFlow, and secure, low‑latency agent infra designs for future large‑scale models.
