AMD Developer Cloud

AMD Developer Cloud is a cloud-based GPU platform that gives developers on-demand access to AMD Instinct accelerators. It is designed for AI researchers, engineers, and builders who need high-memory GPU compute for training, fine-tuning, and inference without managing physical hardware. Members of the AMD AI Developer Program receive $100 in credits to start building immediately.

General
Author	AMD
Type	Cloud GPU Platform
Access	AMD AI Developer Program
Documentation	AMD Developer Cloud Overview
Hardware	AMD Instinct MI300X (192GB HBM3)
Pricing	$100 credits for Developer Program members; pay-as-you-go available

Start building with AMD Developer Cloud

AMD Developer Cloud lets you access AMD Instinct MI300X GPUs through a simple cloud interface, so you can focus entirely on building rather than configuring infrastructure. The MI300X features 192GB of HBM3 memory, making it practical for running 70B+ parameter models on a single instance without model parallelism. Sign up for the AMD AI Developer Program to claim your credits and start running workloads today. Explore what the community has already built on AMD at AMD Use Cases and Applications.

AMD Developer Cloud Tutorials

Getting Started

AMD AI Developer Program Sign up to access $100 in cloud credits and AMD Instinct GPU instances
AMD Developer Cloud Overview Platform documentation and getting started guide
ROCm Documentation Software stack reference for running AI workloads on AMD GPUs
From Zero to AI Builder with AMD Free training courses and hands-on labs with real GPU access

Key Use Cases

Fine-tuning LLMs Use AMD Instinct MI300X instances to fine-tune open-source models such as Llama, DeepSeek, Mistral, and Qwen using PyTorch and ROCm. Hugging Face Optimum-AMD provides optimized training pipelines for AMD hardware.

Large model inference The MI300X's 192GB HBM3 memory capacity supports running very large models on a single GPU, reducing the need for multi-GPU serving setups.

Benchmarking and prototyping Test AI workloads on AMD hardware before moving to on-premises infrastructure. The pay-as-you-go pricing keeps experimentation costs low.

Hackathon development During AMD-sponsored hackathons on lablab.ai, participants receive cloud credits to access AMD GPUs directly through AMD Developer Cloud. Explore upcoming AI hackathons to find events using AMD infrastructure.

SAAS Fashion Garment Marketing Digital Control

SAAS SCM V4 is a Digital Control Tower for Indonesian fashion garment - built for Kultur Agenda competition. Problem: Fashion SMEs (Batik, garment, konveksi) struggle with manual control - from Bahan (Bal/Yard/Kodi), production OEE, Sritex-style invoice/nota/surat jalan, to digital marketing - all disconnected. Solution: One SAAS prototype that controls end-to-end: 1. DESIGN: AI generates Batik motif trends using Vultr Inference (image generation) on AMD - preserving Indonesian cultural heritage while forecasting global trends 2. PRODUCTION: OEE 95%, ERP accurate, KPI tracking - from Kain Bal to Yard to Kodi - with Ruach HaKadosh ethical SOP 3. MARKETING: Digital control - Agent Rush automates marketplace posting, pricing, stock sync 4. DISTRIBUTION: Auto invoice, nota, surat jalan - template SRITEX V4.6 integrated with Google Drive & MS Office Tech: Streamlit + Vultr Serverless Inference API (https://api.vultrinference.com/v1) + AMD Instinct for low-cost inference. OpenAI SDK compatible - one API for chat, vision, images. Impact: Empowers 10M+ Indonesian fashion SMEs to go digital without losing cultural identity - from local Batik to global market. Prototype already active with SAAS_SCM_V4_Full_Accurate sheet. Built for AMD Challenge - Cultural + Commercial.

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Ariadne

Ariadne is a production-grade, proof-grounded web information retrieval agent. Most LLM-based web agents suffer from a critical flaw: they hallucinate. When they cannot find an answer, they guess, leaving users with confident but fabricated lies. Ariadne solves this using a strict "Proof or Silence" architecture. When given a starting URL and a natural language question, Ariadne navigates the website using information-scent scoring. It extracts candidate answers using a three-tier pipeline: deterministic schema.org JSON-LD parsing, LLM-based reading comprehension (via the Gemini API), and a conservative rule-based fallback. Crucially, Ariadne is built with generator-verifier separation. The LLM that extracts the answer is never allowed to declare it correct. An independent Verifier enforces a strict verbatim-quote gate (the quote must exist exactly in the fetched HTML), an attribute-attachment gate, and an independent entailment check. If any gate fails, the agent explicitly returns "not_found". Every successful query returns a "proof object" containing the answer, the exact source quote, the URL, and a SHA-256 content hash, ensuring 100% auditability. If no proof exists, it refuses to guess. Engineered for hostile web environments, Ariadne includes SSRF guards to block private IP fetching, NFC Unicode normalization for international text matching, and strict body-size limits to prevent DOM/resource exhaustion. Packaged on the mandated ROCm base image, Ariadne leverages GPU-accelerated inference via Gemini, ensuring rapid, deterministic, and hallucination-free retrieval that enterprise applications can actually trust.

AMD Volta: Intelligent Web Retrieval Agent

AMD Volta is an autonomous multi-hop web retrieval and synthesis engine engineered for technical research and hardware benchmarking, built for AMD AI League Match 4. Key Innovations Solved: 1. Deep DOM Table Extraction: Real hardware benchmarks live inside HTML <table> tags. Standard text chunkers flatten them into noise. Our DOM parser converts nested HTML tables into intact, atomic GitHub-flavored Markdown tables, preserving comparison matrices and bandwidth specs. 2. Resilient Multi-Engine Cascading: External search APIs fail under rate limits. Volta cascades seamlessly across DuckDuckGo API, DuckDuckGo Lite, Wikipedia, and ArXiv with user-agent rotation to eliminate unhandled crashes. 3. Hybrid BM25 & Semantic Reranking: Combines mathematical Okapi BM25 keyword matching with dense semantic similarity and deduplication. 4. Autonomous Multi-Hop Reasoning: Dynamically assesses evidence sufficiency after Hop 1, reformulates search intent, and executes Hop 2 deep retrieval when necessary. 5. Anti-Hallucination Abstention Guardrail: When queried about fictional hardware (e.g., AMD Radeon RX 19990 XTX), the agent triggers an explicit Abstention Notice, ensuring 100% factual integrity. Hardware Acceleration via AMD Instinct & ROCm: - AMD Developer Cloud (Instinct MI300X): Leverages 192GB HBM3 memory and 5.3 TB/s bandwidth to serve 70B+ LLMs via vLLM with FlashAttention-2 HIP kernels. - Local AMD ROCm: Direct GPU memory access for local embeddings and inference on Radeon / Instinct GPUs. - Universal Fallback: Zero-setup deterministic extractive engine for instant evaluation.

SourceTrace Web Retrieval Agent

SourceTrace is a web information retrieval agent built for Match 4 of the AMD AI League. A user asks a question in plain language, for example a specific fact that lives on a website. The agent plans its own steps: it searches the web, picks the most relevant results, opens the pages, reads the cleaned text, and decides whether it has enough evidence. When it does, it returns a short, exact answer together with the source URLs it actually opened, so every claim can be checked. If a page fails to load or a search returns nothing, the agent recovers and tries another query instead of crashing, and a step limit keeps compute and token use under control. The project is written in plain Python with no heavy dependencies, talks to any OpenAI-compatible model endpoint, so it can run against models served on AMD GPUs, and ships with a CLI, a small HTTP API and a Dockerfile. An offline mock mode and unit tests make it easy to verify the agent loop without a network connection.

LifeOS AI — Personal Digital Twin

LifeOS AI is an intelligent personal digital twin designed to transform how people manage their daily lives using artificial intelligence. It learns user-approved preferences and remembers important information while helping users organize tasks and achieve long-term goals. Unlike traditional AI assistants LifeOS AI maintains personalized context to deliver relevant recommendations across conversations. The platform combines persistent memory with AI agents for task planning and knowledge retrieval. Its Personal Memory Engine stores approved information while Retrieval-Augmented Generation (RAG) retrieves relevant context from personal documents. The Intelligent Planning Agent converts goals into actionable steps and prioritizes tasks based on deadlines and preferences. A Goal Tracking Agent monitors progress and suggests adjustments when plans change. LifeOS AI uses large language models to understand natural language and generate contextual responses. Its agent orchestration system coordinates specialized agents to research information and execute approved actions through connected tools. The platform aims to integrate calendars and productivity applications for seamless task management. The project explores AMD GPU acceleration using compatible open-weight models and ROCm with PyTorch where supported. Performance will be evaluated through response latency and task completion rates alongside retrieval quality and resource utilization. Privacy remains a core principle. Users can inspect and delete stored memories and control connected services. The system will distinguish verified information from AI-generated suggestions and request approval before consequential actions. Built with React and Python or Node.js alongside MongoDB and vector search technologies LifeOS AI aims to create a personalized intelligence layer that makes everyday decisions easier and productivity smarter. LifeOS AI — Think Ahead. Act Smarter. Live Better.