The article explains how NVIDIA Dynamo tackles the production bottlenecks of Agentic AI by using KV‑Cache‑aware routing, a three‑stage multimodal inference architecture, and intelligent cache scheduling on Kubernetes to improve multi‑node throughput (MFU) while maintaining latency SLAs.
Developers deploying Agentic AI face multi‑turn latency caused by repeated token recomputation, KV‑cache eviction, and cold‑starts, and NVIDIA Dynamo 1.1 addresses these issues with KV‑cache‑aware routing, multi‑level cache offload, priority scheduling, and Prefill/Decode separation, as demonstrated in an upcoming Kubernetes‑based live session.
vLLM’s new native RL API introduces a four‑stage weight‑transfer protocol, pluggable backends, and a keep‑mode pause/resume mechanism that eliminates deadlocks in DPEP deployments, with large‑scale validations on SkyRL and Prime‑RL demonstrating reliability and performance gains.
A joint Moonshot AI and Tsinghua study shows that the Prefill-as-a-Service (PrfaaS) architecture, enabled by hybrid‑attention models that shrink KVCache size, can offload long Prefill work to a remote cluster and, with dual‑timescale scheduling, achieve a 54% throughput gain over homogeneous PD deployment and 32% over naive heterogeneous setups.
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.
The article explains how the newly merged Context Parallelism (CP) technique in SGLang, combined with DeepSeek V3.2's Sparse Attention architecture, reduces first‑token latency by up to 80% and alleviates memory pressure for ultra‑long 128K‑token sequences, detailing both algorithmic innovations and engineering solutions.
The article details Baidu Baige’s next‑generation distributed inference platform for trillion‑parameter LLMs, explaining how automated orchestration, the FedDeployment abstraction, SplitService unified view, Adaptive HPA predictive scaling, Silent Instances for second‑level activation, and the Staggered Batched Scheduler eliminate scaling limits, reduce TTFT by 30‑40%, boost throughput by up to 20%, and achieve cost‑effective, elastic AI compute.
This article walks through the DeepSeek‑V3.2‑Exp inference codebase, detailing its MoE architecture, Multi‑Head Latent Attention, FP8 quantization, custom CUDA kernels, and 8‑GPU NCCL‑based distributed execution from initialization through prefill and decode stages.
The article explains how the Dolphin AI platform tackles large‑model deployment challenges by replacing standard Kubernetes Deployments with LeaderWorkerSet, detailing its architecture, features, installation steps, example configurations, testing, scaling, rolling updates, fault recovery, and future roadmap for AI workloads.
This article presents a step‑by‑step guide for deploying and optimizing large‑language‑model inference across multiple GPU‑enabled nodes using ACK Gateway with Inference Extension, vLLM’s tensor‑ and pipeline‑parallel techniques, and Kubernetes resources such as LeaderWorkerSet, PVCs, and custom routing policies, followed by performance benchmarking and analysis.
This guide explains how to use the AIBrix distributed inference platform to deploy the massive DeepSeek‑R1 671B model across multiple GPU nodes, covering cluster setup, custom vLLM images, storage options, RDMA networking, autoscaling, request handling, and observability, turning a weeks‑long deployment into an hour‑scale process.
The article reviews high‑performance inference strategies for large language models such as Deepseek‑R1, detailing CPU‑GPU process separation, Paged and Radix Attention, Chunked Prefill, output‑length reduction, tensor‑parallel multi‑GPU scaling, and speculative decoding, each shown to markedly boost throughput and cut latency in real deployments.
This article introduces Exo, an open‑source platform that lets you turn idle smartphones, tablets, and laptops into a distributed AI cluster capable of running large language models, and shows how Open WebUI provides a user‑friendly interface for deploying private AI assistants.
This article explains the background, challenges, and high‑performance optimization methods for deploying large language and multimodal models, covering inference workflow analysis, distributed concurrency, latency reduction, quantization strategies, and service throughput improvements to achieve industry‑leading speed and memory efficiency.
This article presents Ant Group's large‑scale financial knowledge graph platform, detailing its semantic knowledge representation, hybrid graph model, distributed management architecture, core capabilities such as knowledge evolution and cross‑domain fusion, and showcases applications like anti‑fraud capital‑flow analysis and future DataFabric‑oriented knowledge sharing.
This guide walks through deploying a Bloom 7B1 large language model for distributed inference on Alibaba Cloud Container Service (ACK) using DeepSpeed, Arena, and Kubernetes, covering environment setup, model configuration, service launch, verification, and Ingress exposure.
This article reviews deep learning and AI frameworks, highlights challenges of online model serving, and presents Avior—a lightweight, distributed inference engine designed for high‑performance AI services, detailing its architecture, layer design, benchmark results, and future development plans.
