ROCm

ROCm (Radeon Open Compute) is AMD's open-source software platform for GPU-accelerated computing. It is the AMD equivalent of NVIDIA's CUDA and provides a complete stack for running AI, machine learning, and HPC workloads on AMD GPUs. ROCm supports major ML frameworks including PyTorch, TensorFlow, JAX, and ONNX Runtime, and includes the HIP (Heterogeneous-compute Interface for Portability) programming model for writing GPU code that runs on both AMD and NVIDIA hardware.

General
Author	AMD
Type	Open-source GPU Computing Platform
Documentation	ROCm Docs
Repository	github.com/ROCm
Installation	ROCm Installation Guide
Current Version	ROCm 7
License	MIT and Apache 2.0

Start building with ROCm

ROCm gives you a complete software stack to run AI training and inference workloads on AMD GPUs. It integrates directly with PyTorch, TensorFlow, and JAX so most standard pipelines run with minimal changes from a CUDA environment. Hugging Face Optimum-AMD and vLLM both support ROCm, making it straightforward to run transformer inference and fine-tuning jobs on AMD hardware. Check out the community-built AMD Use Cases and Applications to see what developers are running on ROCm today.

ROCm Tutorials

Documentation and Resources

ROCm Documentation Full reference for installation, APIs, and libraries
ROCm Installation Guide Step-by-step setup for supported Linux distributions
ROCm GitHub Open-source repositories, examples, and issue tracking
HIP SDK SDK for writing portable GPU code for both AMD and NVIDIA hardware
AMD Developer Hub Guides, training videos, and cloud credits

Framework Support

PyTorch Full support for training and inference, including integration with Hugging Face Accelerate and PEFT
TensorFlow GPU-accelerated training and inference on AMD hardware
JAX Supported via the ROCm JAX build
ONNX Runtime Cross-framework model deployment on AMD GPUs
Hugging Face Optimum-AMD Optimized inference and fine-tuning pipelines for transformer models
vLLM High-throughput LLM serving with a ROCm backend

Libraries

hipBLAS BLAS implementation for AMD GPUs
MIOpen Deep learning primitives library for AMD GPUs
rocRAND Random number generation for AMD hardware
hipSPARSE Sparse matrix operations on AMD GPUs
rocBLAS BLAS implementation optimized for AMD Instinct accelerators

Trifecta Checker - Blast Radius Zero

Trifecta Checker is an AI Safety Gate that prevents Lethal Trifecta attacks: when an AI agent reads private data (Gmail), fetches untrusted content (malicious URL), and tries to exfiltrate externally. How it works on Vultr: - rules/trifecta-scoring.js calculates open score 0-100: CLEAN/OK/RISKY/DANGER - rules/shield.js implements Blast Radius Zero: If agent has 3+ Lethal attempts in 1 hour, block ALL Vultr VM creation for 24h - Every block returns HTTP 403 and logs to Concordium wallet 3eLK...z44V for on-chain audit Proof on 【entity-AMD¦canonical_name=AMD】 MI300X GPU (Nov 3): Tested on notebooks.【entity-amd¦canonical_name=AMD】.com Agents 101 - Build AI Agent with MCPs using vLLM, Pydantic AI, MI300X. Safe 【entity-Airbnb¦canonical_name=Airbnb】 search = 20 CLEAN 200, Lethal read_gmail+mcp_airbnb+send_email = 100 DANGER LETHAL 403 BLOCKED. Trusted Source Scoring: .gov=100 .edu=90 concordium_verified=95 .com=40 unknown=10. Web Vault concept: Internet as persistent storage (vector DB), 【entity-AMD¦canonical_name=AMD】 ROCm model, Trifecta Safety Gate. QuantOrb-inspired open rules. Live V2: trifecta-checker-v2-eta.vercel.app GitHub: github.com/altima261/trifecta-checker (5 deploys from mobile, PC broken!) Testnet: 4BdmwCQ1yq...2QM1 (Concordium) DEV: dev.to/altima261/i-ran-trifecta-on-【entity-amd¦canonical_name=AMD】-mi300x-from-an-ipad-web-vault-proof Top 2.3% in 【entity-AMD¦canonical_name=AMD】 AI League (150/6368) - Mobile-first from Jeddah, SA.

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.

Trifecta Checker AI Safety Gate for Lethal Agents

Trifecta Checker is an AI safety gate that blocks Lethal Trifecta attacks - the dangerous combination of Private Data + Untrusted Content + External Communication that enables data theft via prompt injection. The problem: AI agents with tools like read_gmail, browse_web, and send_email can be tricked by malicious web content to steal private emails and exfiltrate them. This is called the Lethal Trifecta. Our solution: One API call - POST /api/check-trifecta - analyzes the agent's tool manifest and returns instant verdict: - LETHAL (3/3 tools): 403 Forbidden - BLOCKED - Cannot execute - WARNING (2/3 tools): 200 OK with caution flag - SAFE (1/3 tools): 200 OK - Safe to proceed Live and tested: https://trifecta-checker.vercel.app/ - LETHAL test [read_gmail, browse_web, send_email] -> 403 - WARNING [browse_web, send_email] -> 200 warning - SAFE [read_gmail] -> 200 safe For 【entity-AMD¦canonical_name=AMD】 AI League Match 4 - Intelligent Web Information Retrieval - we extend with Trusted Source Verification: before allowing external communication, we verify web sources are trusted (.gov, .edu, Concordium ID verified). If source is untrusted, block or flag. Tech: Next.js 14 + TypeScript, Vercel Edge, Python/JS SDKs, 【entity-AMD¦canonical_name=AMD】 ROCm ready for LLM inference. GitHub: github.com/altima261/trifecta-checker. Blog: dev.to/altima261. Built by Mohammed Binashwan (altima261) from Jeddah, Saudi Arabia. Team trifecta-checker solo UTC+3. This project also registered in Concordium Trusted Agents (ZK Proofs + on-chain audit) and Vultr Agent Rush (Blast Radius Zero sandbox).

ROCm Plate & Sign OCR (AMD AI League)

An OCR container for AMD AI League Match 2 that reads licence plates and road signs with an open vision-language model running on an AMD GPU through ROCm. How it works: the container starts a resident server that loads Qwen2.5-VL-7B-Instruct once, in bf16 on the GPU, runs a warm-up inference and then listens on a unix socket. Each call to app.py is a thin client, so the harness can start a new process per image without the model ever being reloaded. Image handling accepts PNG, JPEG and TIFF (including 16-bit and multi-page files), applies EXIF rotation, and resizes very large or very small images to keep the number of visual tokens, and the time per image, bounded. The prompt encodes the transcription rules: return the plate number without state names or slogans, keep the province character and letter on Chinese plates, keep every word printed on a sign, and join multi-line text top to bottom. Post-processing removes labels, quotes and explanations, and folds look-alike separators and full-width characters that the grader's normalization would not remove. Reliability: the model weights are baked into the image, so it runs with no network access. Every call writes a valid JSON file, even when something fails. The build checks that torch and torchvision are ROCm builds. The image is built and contract-checked in GitHub Actions, and evaluated against the sample images plus blurred, noisy, glare, low-light and skewed variants.

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Veriweb: Research You Can Trace

Veriweb helps researchers compare information across the web without losing the evidence behind an answer. A user enters a research question, and Veriweb breaks it into focused searches, extracts full article text, and combines BM25 keyword ranking with vector embeddings to retain relevant passages. The report presents requested comparisons with clickable citations that open the exact supporting quotation beside the answer. Its conflict matrix pairs differing figures with their original passages, helping users distinguish genuine disagreements from differences in dates, units, or scope. Missing evidence remains visible, and unsupported questions can produce an explicit abstention. Citations establish provenance rather than automatically certifying a source as correct. Built and demonstrated on an AMD Ryzen 5 5600H and Radeon RX 6500M with 4GB VRAM, Veriweb runs quantized Qwen2.5-3B locally through Vulkan. Standalone generation measured 36.7 tokens per second. ROCm PyTorch computation was verified separately. The project includes a Next.js research dashboard, FastAPI backend, attributed Markdown export, and a bounded CLI that writes structured JSON. All 40 controlled synthetic research cases passed in the latest complete evaluation. These results are regression evidence, not a claim of universal live-web accuracy.