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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
AuthorAMD
TypeCloud GPU Platform
AccessAMD AI Developer Program
DocumentationAMD Developer Cloud Overview
HardwareAMD 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

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.

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amd AMD Developer Cloud AI technology Hackathon projects

Discover innovative solutions crafted with amd AMD Developer Cloud AI technology, developed by our community members during our engaging hackathons.

AetherDev Pro

AetherDev Pro

AetherDev Pro is an advanced, production-ready multi-agent software development platform and interactive IDE designed for automated software engineering workflows. Built on a Flask backend and a premium glassmorphic HTML/CSS/JS frontend, the platform integrates Microsoft Monaco Editor (the core engine of VS Code) to allow developers to view, edit, and save generated files in real-time. Key Features & Agent Workflow: 1. **Multi-Model Agent Teams**: Users can customize their AI engineering team by routing specific LLMs (e.g. Google Gemini 1.5 Pro, Llama 3.3 70B, GPT-4o) to specialized roles: - **Planner Agent**: Analyzes prompts and outputs structural design layouts and DAGs. - **Engineer Agent**: Automatically implements code for planned files. - **Reviewer Agent**: Evaluates syntax, error handling, and logical correctness, requesting iterative improvements. - **Tester Agent**: Autonomously writes test suites using python's unittest framework. - **Documenter Agent**: Generates comprehensive README files and code documentation. 2. **Self-Healing Code Compilation (TDD Loop)**: AetherDev Pro executes generated test suites in a secure local sandbox subprocess. If any test fails, the error traceback is dynamically parsed and fed back to the Engineer agent with instructions to repair the codebase. This loop repeats autonomously until all tests pass, ensuring that the final output is verified and functional. 3. **Stateless Persistence (SQLite)**: All sessions, file trees, source contents, run records, and terminal logs are persisted in a local SQLite database. This keeps the application robust, resilient to server restarts (such as on cloud platforms like Render), and allows users to resume past projects seamlessly.

Band Memory

Band Memory

Band Memory gives multi-agent systems persistent, shared memory so they stop starting every task with amnesia. The problem: AI agents coordinated through Band.ai handle complex workflows — planning, executing, reviewing — but when the session ends, everything they learned evaporates. The Planner forgets which architecture decisions worked. The Executor re-discovers conventions it already learned. The Reviewer can't reference past findings. Every task starts from zero. Band Memory wires Mimir — a battle-tested persistent memory engine (Rust, SQLite+FTS5, 23 MCP tools) — directly into Band agents. Three agents (Planner, Executor, Reviewer) coordinate through Band rooms and share a common memory backend. Each agent has custom tools (remember, recall, forget) that persist and retrieve context across sessions. In the demo, Session 1 starts cold: the Planner checks for past auth decisions and finds nothing, creates a plan from scratch, the Executor establishes conventions (bcrypt, JWT patterns), and the Reviewer stores findings. In Session 2, the user asks to add OAuth — the Planner instantly recalls the auth architecture, the Executor pulls up the exact conventions, and the Reviewer cross-references past findings. The team compounds knowledge every run. Built with the Band SDK (LangGraph adapter), Mimir MCP server, and GPT-4o for agent reasoning. The skill file in agents/memory_tools.py can be reused by any Band agent. Zero cloud dependency for memory — Mimir runs locally on SQLite. This is what Band agents are missing: memory that survives the session. Not just structured chat history, but searchable, decaying, confidence-scored knowledge that compounds across every interaction.

Bellwether

Bellwether

Problem. Mid-market procurement teams cover 200–2,000 active suppliers on $50M–$500M of annual spend, and review them once a year. By the time a supplier blows up — layoffs, lawsuit, CFO churn, sanctions hit — the buyer finds out from a missed delivery, not from a monitoring tool. One avoided supplier blowup pays for 35 years of Bellwether on a 200-supplier list. What it does. Every morning at 06:00 local, Bellwether wakes up and per-supplier: 1. CrewAI swarm of 4 agents (Researcher / Compliance / Analyst / Writer) fans out 2. Bright Data pulls SERP, LinkedIn, Web Unlocker evidence with provenance per record 3. OFAC SDN list fetched directly from Treasury — deterministic match, never LLM-judged 4. IBM Granite 3.1 8B Instruct on AMD MI300X (vLLM, JSON-mode) extracts structured risk signals from the evidence 5. Deterministic Python scorer (~40 lines, unit-tested) weights signals into a 0–10 score with a 7-day delta 6. Markdown memo written with every score hyperlinked to its source URL + fetch timestamp 7. Perplexity Comet drives the buyer's HubSpot tenant in-browser to file the Supplier Review ticket and assign the account owner — HubSpot REST as fallback if no Comet session token MCP-native. Bellwether ships a FastMCP server exposing `query_supplier_risk(supplier_id)` and `list_suppliers()` — a buyer's CFO can ask Claude Desktop "what's the current risk on Acme?" and get the cited memo back without leaving their tool. Auditable by design. The model extracts; deterministic Python decides. Sanctions hits pin the score at 10 via exact string match against the official OFAC list — Granite is never allowed to decide a regulatory verdict, only to describe one. Every claim in every memo carries `source_url`, `fetched_at`, `scraper_id`. Cost envelope. ~$6/month per supplier all-in (Bright Data + MI300X). One hour of an analyst is $95. Live demo (judge-touchable artifact): https://bellwether-demo.vercel.app/acme

DentAssist AI

DentAssist AI

DentAssist AI is an intelligent dental health platform designed to improve access to oral healthcare through AI-powered symptom assessment, dental image analysis, patient education, and clinic discovery. The platform helps users understand dental concerns, estimate urgency levels, and take appropriate next steps before seeking professional care. By combining dental triage, AI assistance, educational resources, and clinic navigation in one user-friendly application, DentAssist AI bridges the gap between symptoms and treatment, making oral healthcare more accessible, informed, and patient-centered.

AluminatiEye

AluminatiEye

AluminatiEye is a GPU Cloud Intelligence Oracle built to help AI teams make smarter infrastructure decisions in an increasingly complex GPU market. Today, AI builders face fragmented cloud providers, constantly changing GPU pricing, infrastructure shortages, and limited visibility into which provider is the best fit for a workload. Teams often spend hours comparing vendors, researching companies, monitoring pricing, and evaluating risk before deploying models. AluminatiEye creates a unified intelligence layer across the GPU ecosystem. The platform collects and analyzes data from multiple GPU cloud providers and public sources to generate actionable infrastructure insights. Key capabilities include: • Live Pricing – Tracks GPU pricing across multiple cloud vendors in real time. • Arbitrage Detection – Finds cost-saving opportunities between providers. • Market Intelligence – Aggregates news, sentiment, regulations, and competitive signals. • Risk Scores – Evaluates providers based on reliability, growth, uptime, and market health. • Cost Calculator – Estimates infrastructure spending. • Recommender – Suggests optimal GPUs and providers for training, fine-tuning, inference, and image generation workloads. • Oracle Engine – Combines all signals into a single recommendation. Built using Bright Data's web intelligence infrastructure, AluminatiEye transforms raw infrastructure data into strategic recommendations that help organizations reduce costs, mitigate risk, and make faster infrastructure decisions. Our vision is to become the intelligence layer for the GPU economy, giving founders, engineers, researchers, and AI teams a single source of truth for cloud infrastructure decisions.

Apohara CONSILIUM — Agent Governance OS

Apohara CONSILIUM — Agent Governance OS

THE PROBLEM. Italian banks face dual regulatory urgency: DORA (mandatory since Jan 17 2025 for 22,000+ EU financial entities — UniCredit, Intesa Sanpaolo as G-SIBs) + EU AI Act Article 14 (enforceable Aug 2 2026, fines up to €35M or 7% of global revenue). When Banca d'Italia asks "why did your AI decide X?", the bank needs tamper-evident, third-party-signed audit evidence. Existing tools (Galileo $73M, Lakera $30M, Patronus $20M, Credo AI) don't generate court-grade compliance evidence from production runtime. THE SOLUTION. CONSILIUM is a 3-tier open-source platform: (A) OSS Apache-2.0 entry — 9-vendor adversarial LLM ensemble + 78-rule deterministic judge layer + INV-15 Z3 SMT formal proof (UNSAT in 10.08ms). (B) Governance OS core — 4-stage SOAR pipeline + 6 compliance framework dashboards (EU AI Act, NIST AI RMF, ISO 42001, SOC 2, GDPR, NIST 800-53) + HMAC-SHA256 verdict chain + STIX 2.1 export. (D) CAICEP module — RFC 3161 TSA-timestamped verdict chain via freetsa.org (live evidence today) + roadmap to court-admissible attestation Q3 2026 via eIDAS QTSP partnership (Actalis Italia). LIVE EVIDENCE. apohara.dev/consilium/verify — interactive demo: paste any prompt → 9-vendor decision. Click any of 3 demo verdicts → verify RFC 3161 timestamp against freetsa.org independently. api.apohara.dev shows 10+ SOAR endpoints live, /v1/verdicts/{hash}/verify-timestamp returns valid:true with real Freetsa.org-signed token (1312 bytes, signed 2026-05-19T12:21:50Z). BUSINESS VALUE. TAM AI governance $3.59B by 2033 (36% CAGR). SAM EU regulated industries $400-800M by 2027. Initial wedge: Italian G-SIBs + Milan Fintech District (200+ companies) = $15-30M ACV in 12 months. Revenue: OSS free + Cloud Pro $299-999/mo + Business $2-5K/mo + Enterprise+CAICEP $25-200K/year. Exit reference: Cisco acquired Robust Intelligence Aug 2024 (~$350M, 451 Research). Built solo by Pablo M. Suarez (UNT, Argentina) in 8 days for Milan AI Week 2026.