This article provides a step‑by‑step technical walkthrough of vLLM 0.8.4 on a single GPU, detailing the platform’s startup, model loading, Multi‑LoRA deployment, internal ZMQ communication, request scheduling, and inference execution, while exposing key source‑code snippets and architectural diagrams.
This tutorial walks through setting up a local k3d cluster, installing Higress, and using its AI plugins—including AI Proxy, AI JSON formatter, AI Agent, and AI Statistics—to integrate and observe Alibaba Cloud's Qwen large language models across various use cases such as weather and flight queries.
This guide explains how to quickly deploy the new Qwen3.5-397B-A17B open‑source large model using Alibaba Cloud Function Compute's serverless GPU service, covering model features, deployment steps, required commands, and performance benefits.
This article explains what NVIDIA NIM is, compares its generous free quota to other LLM providers, lists the supported free models, walks through a five‑minute sign‑up, shows three code examples for calling the API, offers model‑selection advice, and provides a hands‑on case for building a free AI chat interface.
This article explains why traditional load‑balancing methods fall short for large language model services and introduces Higress AI Gateway's three specialized algorithms—global minimum‑request, prefix‑matching, and GPU‑aware load balancing—detailing their design, Redis‑based implementation, deployment steps, and performance gains.
This report details the analysis of memory bottlenecks in DeepSeek‑V3.2‑Exp, proposes the Expanded Sparse Server (ESS) that offloads latent cache to CPU memory, and demonstrates through high‑fidelity simulation that the approach, combined with cache‑warmup and overlap techniques, can double decoding throughput for long‑context inference.
This guide explains how to deploy open‑source large language models using vLLM, benchmark latency and throughput, and apply 8‑bit/4‑bit quantization techniques such as BitsandBytes and NF4 to achieve faster inference on limited‑GPU hardware.
This tutorial walks through deploying the Llama‑2‑7b‑hf model on Alibaba Cloud Kubernetes (ACK) using KServe, Triton Inference Server with the TensorRT‑LLM backend, covering prerequisites, model preparation, YAML configuration, PV/PVC setup, runtime creation, and troubleshooting steps.
This guide walks you through deploying the open‑source QwQ‑32B model on Alibaba Cloud Function Compute with CAP, covering required services, step‑by‑step deployment, cost notes, accessing the demo UI, interacting with the model, scaling settings, and resource cleanup.
This article analyzes the challenges of deploying large language models locally and presents a comprehensive set of engineering techniques—including CPU/GPU process separation, Paged Attention, Radix Attention, chunked prefill, output‑length reduction, multi‑GPU tensor parallelism, and speculative decoding—to dramatically boost inference throughput and cut response latency.
This curated reading list compiles essential papers on AI agents, task planning, hallucination mitigation, multimodal models, image/video generation, foundational LLM research, open‑source large models, fine‑tuning techniques, and performance optimization, providing a comprehensive roadmap for anyone aiming to master modern generative AI.
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.
HyperAI’s tutorial lets developers instantly launch the open‑source Gemma‑4‑31B model—supporting multimodal input, up to 256 K token context and over 140 languages—through a one‑click deployment on RTX 6000 or RTX 5090 GPUs, with detailed step‑by‑step instructions and optional compute credits.
This guide shows how to quickly deploy the open‑source FastChat AI assistant on Alibaba Cloud ASK's serverless Kubernetes platform, covering prerequisites, YAML configuration, GPU handling, verification steps, and three usage scenarios including web UI, API calls, and a VSCode extension.
This guide walks you through deploying the open‑source QwQ‑32B model on Alibaba Cloud Function Compute using the Cloud Application Platform (CAP), covering architecture, required services, account setup, step‑by‑step deployment, cost considerations, model interaction via Open WebUI and Chatbox, scaling configuration, and resource cleanup.
This guide walks you through the end‑to‑end process of deploying the open‑source QwQ‑32B inference model on Volcengine's cloud platform, covering GPU ECS selection, VKE cluster creation, continuous delivery CP setup, vLLM service launch, and API gateway exposure.
The article details how to deploy the 4‑bit quantized Qwen3.6-35B model with vLLM 0.17 (and 0.19.1 patch) on a Docker container, compares its memory usage and token‑generation speed to Qwen3.5‑35B, and shares practical scripts and observed performance of roughly 150 tokens per second.
This article analyzes the cloud‑native challenges of deploying large‑model inference on Kubernetes and presents Kthena’s architecture—ModelServing, Router, Autoscaler, and ModelBooster—along with Volcano integration, vLLM‑Ascend setup, and a real‑world Qwen3‑235B deployment case, highlighting performance gains and future directions.
This article walks through deploying DeepSeek‑V4‑Flash on a server with two NVIDIA H20 GPUs (96 GB each), detailing model download, Docker image preparation, launch script tweaks, memory compression via FP8 and expert parallelism, and reports observed concurrency limits and token‑per‑second speeds, including a test that disables the model's thinking mode.
This guide walks you through configuring OLLAMA for multi‑GPU load balancing, covering hardware checks, CUDA setup, native and Docker deployment methods, detailed parameter tuning, advanced sharding strategies, troubleshooting, performance optimization, and production‑grade monitoring to maximize throughput and stability of large language models.
