Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

AMD

Advanced Micro Devices (AMD) is a global semiconductor company that designs CPUs, GPUs, and accelerators for data centers, PCs, and embedded systems. Founded in 1969, AMD has built a significant AI infrastructure position through its AMD Instinct GPU line and the open-source ROCm software stack, which together serve as an alternative to proprietary GPU ecosystems for large-scale AI development.

General
CompanyAdvanced Micro Devices, Inc.
Founded1969
HeadquartersSanta Clara, California, USA
Websiteamd.com
DocumentationROCm Docs
GitHubgithub.com/ROCm
Developer HubAMD ROCm Developer Hub
TypeSemiconductor / AI Infrastructure

Start building with AMD products

AMD provides cloud-based GPU access, open-source software tooling, and developer resources for building AI applications at scale. Whether you are training a custom model, running large-scale inference, or benchmarking AI workloads, AMD's infrastructure stack gives you the compute and software you need without proprietary lock-in. Explore what the community has built on AMD by checking out AMD Use Cases and Applications.

Core Products

AMD Instinct GPU Accelerators

The AMD Instinct series are data center GPUs built for AI training and inference at scale. The MI300X is based on the CDNA 3 architecture and supports up to 192GB of HBM3 memory, making it well-suited for large language model inference where memory capacity is a bottleneck. The MI325X extends this to 288GB of HBM3E memory. Seven of the ten largest model builders and AI companies, including Meta, OpenAI, Microsoft, and xAI, run production workloads on Instinct GPUs.

ROCm (Radeon Open Compute)

ROCm is AMD's open-source software platform for GPU-accelerated computing. It supports HIP, OpenCL, and OpenMP programming interfaces and integrates with major ML frameworks including PyTorch, TensorFlow, and JAX. ROCm 7 is the current version, engineered for generative AI and HPC workloads with expanded hardware compatibility and new development tools.

For framework support, installation guides, and libraries, see our ROCm tech page.

HIP SDK

The AMD HIP (Heterogeneous-compute Interface for Portability) SDK allows developers to write GPU-accelerated code that runs on AMD hardware. HIP code is also designed to be portable to CUDA, lowering the barrier for developers migrating workloads from other GPU platforms.

AMD Developer Cloud

AMD provides a cloud environment where developers can access AMD Instinct GPU hardware for testing and benchmarking, along with free credits, training materials, and community support.

For setup details, credit access, and tutorials, see our AMD Developer Cloud tech page.

Developer Resources

AMD's open-source developer ecosystem is built around ROCm, with documentation, libraries, and tooling available for AI and HPC workloads on AMD hardware.

Key Features

Open-source software stack ROCm is fully open-source under the MIT and Apache 2.0 licenses, giving developers full visibility into the toolchain and the ability to contribute upstream.

Large memory capacity The MI300X provides up to 192GB of HBM3 memory per GPU, enabling inference of very large models (70B+ parameter) on a single accelerator without model parallelism.

Framework compatibility ROCm supports PyTorch, TensorFlow, JAX, and ONNX Runtime, allowing most standard AI training and inference pipelines to run without significant modification.

HIP portability HIP code compiles for both AMD and NVIDIA hardware, reducing the cost of maintaining GPU-specific codebases across infrastructure environments.

Use Cases

Large language model inference The high HBM capacity of AMD Instinct GPUs makes them a practical choice for serving large models where VRAM is the primary constraint.

AI model training Teams training custom models at scale use AMD Instinct GPUs through cloud providers and on-premise clusters as a cost-competitive alternative to other data center GPU options.

HPC workloads ROCm's support for scientific computing libraries makes AMD hardware a common choice for high-performance computing in research and enterprise environments.

Hackathon and prototyping AMD provides cloud access and credits for developers building AI prototypes, making it possible to test workloads on AMD hardware without upfront hardware costs. Explore upcoming AI hackathons that use AMD infrastructure.

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amd AI Technologies Hackathon projects

Discover innovative solutions crafted with amd AI Technologies, developed by our community members during our engaging hackathons.

RavenAI

RavenAI

RavenAI democratizes enterprise-grade cybersecurity for SMBs, startups, and developers who can't afford $10K/year tools like Qualys. The problem: 43% of cyberattacks target small businesses, but existing scanners are slow, technical, and require security experts. Founders need instant answers, not 50-page PDF reports. Our solution: A Streamlit-based AI scanner that delivers SSL certificate analysis, security header audits, and domain threat intelligence in 30 seconds. Enter any domain → get instant security grade + AI-generated explanation of risks + copy-paste fix instructions. No cybersecurity degree required. Built with Python, OpenSSL, Whois, Requests, and HuggingFace LLM for threat analysis. Using AMD ROCm GPU credits to power an AI agent that auto-tests XSS/SQLi payloads and generates compliance reports 10x faster than traditional scanners. Freemium model: 3 free scans/day, $29/mo for unlimited + API access. Security shouldn't require a $150K CISO. RavenAI gives every founder a security team in their browser.

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.