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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.

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.

Autonomous AI Agents for CRISPR Diagnostics

Autonomous AI Agents for CRISPR Diagnostics

Our project addresses the critical gap in healthcare accessibility across Africa by leveraging advanced autonomous AI agents to automate and streamline CRISPR diagnostics and complex clinical workflows. Powered by state-of-the-art large language models like Meta-Llama-3-8B-Instruct, the system acts as an intelligent layer capable of parsing complex genetic data, validating clinical outputs, and providing real-time decision support for healthcare providers. By optimizing laboratory diagnostics and handling administrative clinical structures autonomously, the platform dramatically reduces latency times, eliminates manual errors, and guarantees high-precision medical analysis. This ensures that cutting-edge precision healthcare becomes fast, affordable, and scalable for underserved populations.

MusKent Commerce OS

MusKent Commerce OS

MusKent is a production-ready autonomous AI system designed to support real commerce operations across revenue, sales, automation, fulfillment, billing, and marketplace workflows. It moves beyond traditional copilots by combining reasoning agents, async execution, tool orchestration, and multimodal input into a unified operating system. At its core, MusKent uses agent-driven decision flows. It continuously evaluates business signals such as revenue performance, marketplace activity, sales trends, and operational state, then determines the next best action using AI. These agents operate within structured workflows, interact with internal tools and external APIs, and execute multi-step tasks while safely degrading to fallback systems when needed. The platform integrates multiple intelligence layers, including AI-powered reasoning for decision-making, specialized compute for ranking and scoring, and voice-based interaction through real-time and batch transcription. This enables a collaborative multi-agent system where different models and providers handle specific tasks like reasoning, analysis, and fallback execution. MusKent is designed for reliability and real-world usage. It supports asynchronous job processing for long-running operations, structured outputs for consistency, provider health awareness, and safe fallback mechanisms to maintain performance even under degraded conditions. From a systems perspective, MusKent delivers intelligent reasoning, agentic workflows, enterprise utility, and multimodal interaction in a single platform. The result is an AI-powered commerce operating system that can analyze, plan, and act across business operations while remaining resilient in production environments.

Apohara PROBANT — Cross-AI Code Verifier

Apohara PROBANT — Cross-AI Code Verifier

Apohara PROBANT is a cross-AI code verification platform. Gemini writes a review; a 12-vendor adversarial ensemble (Claude, GPT, DeepSeek, Kimi, GLM, Qwen, Nemotron, MiniMax, Big-Pickle, Mistral Large, Grok 2, Perplexity Sonar) independently audits the output for prompt injection, vulnerabilities, and logic bugs. Verifiable, not claimed: - 12 vendors via OpenRouter, each in an isolated KV-cache enforced by INV-15 JCRSafetyGate. Paper v3.0 (formal Z3 SMT proof, UNSAT on negation in 10.08 ms) complements v2.0.1 empirical sweep (0/1210 violations). DOI 10.5281/zenodo.20277875. - JBB-Behaviors block rate 93.75% (Wilson 95% CI [86.2%, 97.3%], n=80 holdout). Numbers from logs/*.json, not marketing. - 120+ pytest tests + 15+ measurement JSON logs. - Multi-hardware: AMD MI300X (ROCm 7.2) + NVIDIA H100. Four hardening layers (auditable in repo): 1. Veea LobsterTrap DPI subprocess pre-check — measured: SQLi block 50% (n=20, CI [29.9%, 70.1%] directional), benign FPR 9.8% (n=51). Live demo SQLi returns verdict=blocked in ~25 ms. 2. Prompt envelope nonce-tagged sentinels (Hines et al. arXiv 2403.14720). 3. HMAC-SHA256 verdict ledger chain. verify_chain() catches tampering. 4. NO-HEDGING gate (HARD/SOFT split, judge uncertainty flagged). Distribution: Cursor / Claude Code plugin shipped as VSIX. MCP server (stdio) for Claude Desktop / Cursor / Zed. /v1/verify_stream SSE for live per-vendor results. /dashboard for ops view. Stack: FastAPI/Python 3.11+, React+Vite + Next.js SSR PoC, Apache-2.0, monorepo across 3 GitHub orgs. Live demo BYOK or 5 free/IP/day.

klarixa-tricortex-amd-hackathon

klarixa-tricortex-amd-hackathon

.### Technical Architecture & Core Overview Tricortex is an enterprise-grade, infrastructure-agnostic AI orchestration core engineered to execute complex multi-model reasoning pipelines. Built using the pydantic-ai framework, the system enforces a strict, non-blocking asynchronous lifecycle over autonomous agents. Rather than deploying volatile, free-form execution loops, Tricortex establishes structural operational boundaries through narrow API integration gates, explicit dynamic schema enforcement, and robust human-in-the-loop validation milestones. ### Cross-Platform Hardware Validation To demonstrate absolute operational resilience, the entire orchestration layer has been cross-validated and stress-tested under high-performance AMD compute cluster configurations. This cross-hardware implementation ensures that the system's token routing, latency management loops, and memory distribution handling remain highly stable across varied cloud infrastructures—such as Vultr nodes—preventing critical Out-Of-Memory (OOM) faults during heavy multi-model execution. ### Multi-Model Brain Integration Tricortex decouples the core reasoning layer from a single provider by leveraging a highly adaptive, multi-model backend topography: 1. Google DeepMind Ecosystem: Integrates advanced Gemini models via Google AI Studio to anchor real-time, context-aware routing decisions. 2. Alibaba Qwen Specialized Intelligence: Dynamically injects specialized model intelligence optimized for precise vertical logic across complex domains like finance, law, and medicine. ### Key Architectural Pillars * Asynchronous Emulation: Implements secure, in-memory execution tracking to simulate dense tensor processing latencies natively, ensuring total code reliability. * Model Context Protocol (MCP) Integration: Bridges the gap between remote LLM environments and local system runtime operations, allowing secure context pathway mapping without exposing raw database states.