# GPUX.AI 依據 OpenAITools.app 於 2026-11-11 查驗的觀測資料，GPUX.AI（gpux.ai）是收錄於 AI API 的 AI 平台，每月產生 68 次造訪，月流量變化量為 -649（-90.5%）。其主要地理市場為印尼（流量佔比 100%），並採用需聯絡報價定價模式。依每月流量計算的主要競爭替代方案包括 BytePlus（每月 303.61萬次造訪），Together AI（每月 164.59萬次造訪），DeepInfra（每月 45.78萬次造訪）。 GPUX.AI is a serverless GPU platform for deploying Dockerized AI applications, running inference on demand and selling access to your own private models. ## 主要觀測資料與規格 | 指標 | 數值 | | :--- | :--- | | 工具名稱 | GPUX.AI | | 代稱 | gpux-ai | | Status | active | | Origin | independent | | Maintenance | active | | 官方網域 | gpux.ai | | 網站網址 | https://gpux.ai | | 分類 | AI API, AI 模型, AI 開發者工具, AI 圖像生成器, AI 語音轉文字, Large Language Models (LLMs) | | 定價模式 | 需聯絡報價 | | 每月造訪次數 | 68 | | 月流量變化 | -649 | | 月成長率 | -90.5% | | 主要市場 | 印尼 | | 市佔率 | 100% | | 直接造訪 | 27 | | 自然搜尋造訪 | 0 | | 社群造訪 | 0 | | 推薦流量造訪 | 6 | | 電子郵件造訪 | 1 | | 評論分數 | 3.5 | | 收藏數 | 0 | | 支援平台 | 網站 | | 帳務平台 | | | 屬性 | 功能: 網站; 價格: 需聯絡報價 | | 標籤 | GPU, Docker, 推論, 無伺服器, AI, 機器學習, StableDiffusion, 模型部署 | ## 總覽 GPUX.AI is a serverless GPU platform for deploying Dockerized AI applications, running inference on demand and selling access to your own private models. ## 價格與方案 Contact for pricing. GPUX.AI does not publish public plan tiers or per-GPU rates on its site; pricing is arranged through a sales conversation, with a 'Talk to a Founder' booking link as the entry point. ## 關鍵功能與能力 - **Dockerized app deployment:** Package AI applications as Docker images and run them on GPU infrastructure with claimed cold starts around one second, so you do not manage servers yourself. - **Serverless inference over HTTP:** Invoke models over HTTP — the site advertises a single curl call for Stable Diffusion XL — and pay for what you use rather than idle GPU time. - **Monetize private models:** Host a private model and sell inference requests to other organizations, turning your model into a product. - **GPU routing and cold starts:** Route inference across GPUs with an emphasis on cold-start performance and efficient scheduling for bursty workloads. - **Prebuilt model examples:** Reference deployments for Stable Diffusion, SDXL, ESRGAN and Whisper show how image generation and speech-to-text workloads are wired up. ## 主要使用情境 - **Serverless image generation (ML engineer):** Run diffusion and upscaling models on demand instead of provisioning and maintaining dedicated GPU servers, paying only for requests served. - **Host and monetize a private model (AI startup founder):** Containerize a model, expose it over HTTP and sell inference requests to other organizations without building billing and routing infrastructure. - **Deploy custom AI containers (Backend developer):** Use Docker to deploy custom pipelines that a fixed-model API cannot support, keeping your existing container tooling. - **Batch transcription workloads (Media or audio team):** Run Whisper-family speech-to-text through an HTTP endpoint as part of a transcription or media-processing workflow. - **Earn on idle GPU hardware (Compute contributor):** Contribute spare Windows or Linux GPU capacity via the open-source edge agent on GitHub. ## 優勢 - Docker-based deployment lets you run arbitrary AI applications, not just a fixed model catalog. - Cold-start performance is a stated core focus, which matters for bursty serverless inference. - Inference is callable over simple HTTP, illustrated by a one-line curl example. - Private models can be monetized by selling requests to other organizations. - Open-source edge agent on GitHub extends the platform's model to spare machines. ## 限制 - Marketing content has not been refreshed recently, so it is hard to tell how actively the platform is maintained. - The hosted dashboard and inference endpoint were not reachable during this audit, which is a real risk for a service you would build on. - Documentation is light compared with larger serverless GPU providers. - 'Contact for pricing' means you cannot self-serve a cost estimate before talking to sales. - Docker-based deployment has a learning curve if you are used to notebook-style workflows. ## 常見問題 ### What is GPUX.AI? GPUX.AI is a serverless GPU platform. You deploy Dockerized applications, run inference workloads such as Stable Diffusion and Whisper, and can sell requests on your own private models to other organizations. It advertises cold starts from around one second. ### How much does GPUX.AI cost? GPUX.AI does not publish public plan pricing; engagement is by contact and a 'Talk to a Founder' booking link. Because it targets GPU workloads sold on usage and private model hosting, cost depends on what you deploy. ### What kind of AI workloads does it run? It is built around Docker, which means you can package arbitrary AI applications rather than only calling a fixed set of hosted models. The site highlights Stable Diffusion XL, ESRGAN and Whisper as examples, including a one-line curl call for SDXL. ### Can I sell access to my own model? Yes. A documented use case is selling requests on your private model to other organizations, so you can monetize a model you host rather than only consuming compute. ### Is the platform actively available? The homepage was live during this audit but the hosted app and inference endpoint subdomains were not reachable at the same time, and the site's own news stops in 2023. Confirm current service availability with the team before committing production workloads. ### GPUX.AI 的每月流量與成長趨勢為何？ GPUX.AI 目前每月有 68 次造訪，月流量變化為 -649（成長率 -90.5%）。其主要來源市場為印尼（佔比 100%），其中包含每月 27 次直接造訪與每月 0 次自然搜尋造訪。 ### GPUX.AI 是免費的嗎？其定價模式為何？ GPUX.AI 採用需聯絡報價定價模式。Contact for pricing. GPUX.AI does not publish public plan tiers or per-GPU rates on its site; pricing is arranged through a sales conversation, with a 'Talk to a Founder' booking link as the entry point. 支援的帳務與結帳平台包括：未列出。 ### 2026 年最佳的 GPUX.AI 替代方案有哪些？ 依據 OpenAITools.app 的分類流量與功能契合度，GPUX.AI 的前五大替代方案為：1. BytePlus（每月 303.61萬次造訪）; 2. Together AI（每月 164.59萬次造訪）; 3. DeepInfra（每月 45.78萬次造訪）; 4. fal.ai（每月 309.37萬次造訪）; 5. Runware（每月 40.29萬次造訪）。 ### GPUX.AI 支援哪些平台與核心 AI 能力？ GPUX.AI 分類於 AI API, AI 模型, AI 開發者工具, AI 圖像生成器，支援以下平台：網站，其核心能力標籤包括：GPU, Docker, 推論, 無伺服器, AI, 機器學習。 ## 替代方案 | 工具名稱 | 每月造訪次數 | 定價模式 | 比較 | | :--- | :--- | :--- | :--- | | [BytePlus](https://openaitools.app/tw/tool/byteplus) | 3,036,127 | 免費增值、付費 | [比較](https://openaitools.app/tw/compare/byteplus-vs-gpux-ai) | | [Together AI](https://openaitools.app/tw/tool/together-ai) | 1,645,930 | 免費增值、付費 | [比較](https://openaitools.app/tw/compare/gpux-ai-vs-together-ai) | | [DeepInfra](https://openaitools.app/tw/tool/deep-infra) | 457,810 | 付費 | [比較](https://openaitools.app/tw/compare/deep-infra-vs-gpux-ai) | | [fal.ai](https://openaitools.app/tw/tool/fal-ai) | 3,093,684 | Paid, pay-as-you-go usage-based pricing | [比較](https://openaitools.app/tw/compare/fal-ai-vs-gpux-ai) | | [Runware](https://openaitools.app/tw/tool/runware) | 402,862 | 付費 | [比較](https://openaitools.app/tw/compare/gpux-ai-vs-runware) | | [Novita AI](https://openaitools.app/tw/tool/novita-ai) | 271,371 | 付費 | [比較](https://openaitools.app/tw/compare/gpux-ai-vs-novita-ai) | | [Replicate AI](https://openaitools.app/tw/tool/replicate-ai) | 904,038 | 付費 | [比較](https://openaitools.app/tw/compare/gpux-ai-vs-replicate-ai) | | [OpenAI 平台](https://openaitools.app/tw/tool/openai) | 234,678,639 | 付費 | [比較](https://openaitools.app/tw/compare/gpux-ai-vs-openai) | | [Chutes](https://openaitools.app/tw/tool/chutes) | 250,187 | 付費 | [比較](https://openaitools.app/tw/compare/chutes-vs-gpux-ai) | | [Synexa AI](https://openaitools.app/tw/tool/synexa-ai) | 7,179 | 付費 | [比較](https://openaitools.app/tw/compare/gpux-ai-vs-synexa-ai) | | [Google AI Studio](https://openaitools.app/tw/tool/google-ai-studio) | 112,620,020 | 免費增值 | [比較](https://openaitools.app/tw/compare/google-ai-studio-vs-gpux-ai) | | [Fireworks](https://openaitools.app/tw/tool/fireworks-ai) | 659,687 | 需聯絡報價 | [比較](https://openaitools.app/tw/compare/fireworks-ai-vs-gpux-ai) | ## 社群 GPUX.AI 社群：交流在 GPU 上執行 AI 推論與 Docker 化應用、透過 HTTP 端點部署模型，以及在 Windows 10 或 Linux 上共享 GPU 算力的經驗。 [GPUX.AI](https://discord.com/invite/jjBSjSF) --- 來源：https://openaitools.app/tw/tool/gpux-ai