This guide shows how to configure Claude Code to run on local or cloud models within five minutes, covering hardware requirements, recommended models, step‑by‑step installation for Ollama, llama.cpp, LM Studio, and cloud‑based options, plus performance and cost comparisons.
This guide walks you through deploying the Qwen3-4B-hygon-flagos model on the open‑source FlagOS stack, pulling the Docker image from Tencent Cloud HAI, configuring OpenClaw, and connecting the model to a QQ bot, while highlighting performance trends and practical considerations.
This guide walks you through installing NVIDIA drivers and Docker, configuring the NVIDIA Container Toolkit, and deploying GPUStack in Docker to manage heterogeneous GPU resources, run large language, multimodal, diffusion, and embedding models, and scale from a single node to a multi‑node GPU cluster.
This article explains the opportunities and challenges of Mixture of Experts (MoE) models, introduces expert parallelism as a solution to scaling and deployment bottlenecks, and provides a step‑by‑step guide for deploying MoE models with Alibaba Cloud PAI‑EAS, including configuration tips and code examples.
This article explains how Alibaba Cloud ACK One's ModelDistribution leverages OCI images to standardize, version, and efficiently distribute large AI models across multiple Kubernetes clusters worldwide, addressing challenges of storage, deployment speed, and pre‑warming for rapid inference services.
This tutorial walks you through using a one‑click script to create a GPU‑enabled Kind Kubernetes cluster, evenly distribute GPU resources across nodes with nvkind, install necessary drivers and toolkits, deploy a vLLM‑served large language model, and verify its operation, all on a local or cloud environment.
This guide walks you through installing NVIDIA drivers, Docker, and the NVIDIA Container Toolkit, then shows step‑by‑step how to run GPUStack in Docker, expand a GPU cluster, and serve large language, multimodal, diffusion, and embedding models with OpenAI‑compatible APIs.
Learn how to efficiently package, build, and run large AI models in Docker containers—from preparing the model and API code, creating Dockerfiles, building and testing images, to scaling in production with Kubernetes and GPU support—complete with step‑by‑step commands and best‑practice tips.
This guide shows how to use the lightweight OpenStation platform to install, configure, and launch the DeepSeek‑R1 large‑model on a personal server in under 15 minutes, covering zero‑code deployment, resource management, inference engine selection, and integration with CherryStudio.
Learn step‑by‑step how to set up a stable, high‑efficiency local text‑to‑image generation service using the Flux model series on Alibaba Cloud’s Bailen platform, integrate it with Cursor IDE’s MCP tool, configure environments, manage API keys, and run the service with sample code and results.
This guide walks you through deploying the open‑source QwQ‑32B inference model using Alibaba Cloud's Serverless AI platform CAP, covering benchmark highlights, preparation steps, two deployment methods (application template and model service), verification, and project cleanup.
This guide walks you through quickly deploying Baidu's DeepSeek‑R1 model, accessing its WebUI, and enabling key enterprise extensions such as web search, file upload, OCR, and content moderation to integrate AI capabilities into production workflows.
This guide walks developers through the full lifecycle of using DeepSeek—choosing the right deployment method (API, local machine, or private cloud), selecting model sizes based on hardware, configuring Tencent Cloud services, building AI applications, and integrating the model into development tools and mini‑programs.
This guide explains how to install Ollama on a Windows machine, configure its environment, and use it to download and run the DeepSeek‑R1 1.5B large language model locally, enabling offline AI interactions without relying on remote servers.
This guide compares DeepSeek‑R1’s 1.5B/7B/8B, 14B/32B, and 70B/671B versions, detailing their characteristics, typical applications, and the specific CPU, memory, and GPU specifications required for local deployment, helping you select the optimal model for your resources.
This guide walks you through deploying the DeepSeek‑R1‑Distill model on Volcengine CPU ECS instances, covering use‑case scenarios, recommended server types, Docker setup, environment configuration, and verification steps to achieve cost‑effective, high‑compatibility AI inference.
This article introduces the state‑of‑the‑art Step‑Video‑T2V text‑to‑video model and the Step‑Audio‑Chat voice interaction model, outlines their technical specifications and benchmark results, and provides a detailed step‑by‑step guide for deploying both models with a single click using Alibaba Cloud's PAI Model Gallery.
This guide walks through installing Ollama on Windows, configuring the OLLAMA_MODELS path, downloading the 7‑b DeepSeek‑R1 model, running it locally, and accessing it via a browser using the Page Assist extension, providing step‑by‑step commands, screenshots, and tips for offline setups.
This guide walks you through creating a lightweight compute instance, adding it to Baidu Baige AI heterogeneous computing platform, deploying the vLLM tool, loading and serving small‑scale dense models such as DeepSeek, Llama and Qwen, and provides recommended configuration lists to achieve low‑cost, high‑performance inference.
This guide walks you through registering on SiliconFlow, selecting DeepSeek models, installing Cherry Studio, configuring API keys, setting up the environment, and testing the AI assistant, enabling a full‑feature local deployment without high‑end hardware.
This guide walks you through installing Ollama, selecting the appropriate DeepSeek‑R1 model size based on GPU memory, running the model locally, and optionally integrating it with Webman AI for a richer user experience.
This guide walks you through preparing a Tencent Cloud account, creating a Cloud Studio workspace, installing Ollama, downloading and running the DeepSeek‑R1 large language model, interacting via terminal or API, and managing resources and model versions.
This guide explains how to install Ollama, download and run the open‑source DeepSeek‑R1 language model locally, configure GPU acceleration, and integrate the model into a Spring Boot application using Spring AI to provide an API service for AI inference.
Deploy DeepSeek‑R1 models on Tencent Cloud HAI in just three minutes by logging in, creating an application, and accessing the model via ChatbotUI or JupyterLab, without purchasing GPUs or configuring environments, while also leveraging integrated services like Cloud Studio and Object Storage for enterprise AI solutions.
This guide explains how to deploy the Qwen2.5 large language model on Alibaba Cloud Function Compute using Ollama and Open WebUI, covering model selection, resource configuration, deployment steps, interface setup, multilingual capabilities, and automatic scaling for high‑concurrency workloads.
This article provides a comprehensive tutorial on fine‑tuning the Qwen‑14B large language model, covering the motivation, fine‑tuning concepts, step‑by‑step workflow, required code, DeepSpeed training parameters, testing scripts, and deployment using FastChat and the 360AI platform.
This guide walks you through using Alibaba Cloud's PAI‑EAS to quickly deploy the open‑source Stable Diffusion text‑to‑image model with a low‑code WebUI, covering preparation, service configuration, inference testing, and common troubleshooting tips for performance and file management.
This guide walks you through the prerequisites, step‑by‑step console and kubectl procedures, YAML configuration, and post‑deployment verification needed to run a CPU‑only Stable Diffusion model on Alibaba Cloud Container Service (ACK) and optionally switch to a GPU‑enabled version.
Shuhe Technology leveraged Alibaba Cloud Container Service with Knative and ECI virtual nodes to achieve auto‑scaling, multi‑version management, and up to 60% cost reduction for AI model services, dramatically improving resource efficiency and stability under burst traffic.
