Multi-Agent Fine-Tuning

🔥 Multi-Agent AI Training on AMD MI300X: Collaborative Intelligence at Scale Our platform trains teams of AI agents simultaneously on shared tasks, enabling them to learn faster through coordinated optimization and comparative feedback. This runs on a single AMD MI300X GPU (192GB HBM), powered by ROCm and the CoMLRL framework, demonstrating how next-gen workloads thrive on AMD infrastructure. Unlike single-model training, our full-stack solution integrates model development, real-time telemetry, profiling, artifact storage, and analytics into one reproducible pipeline. The hosted dashboard exposes training curves, GPU/memory utilization, platform diagnostics, and throughput metrics—all while checkpointing ensures reliability for long-duration RL workloads. Built around open models (HuggingFace Hub) and experiment tracking (W&B/MLflow), the system addresses infrastructure realities: concurrent execution, resource efficiency, and scalability. The result? A practical proof that AMD GPUs power the future of multi-agent AI—with optimizations for even larger, more efficient workloads. Reliability & Reproducibility Failure recovery: Logs proving zero data loss after GPU preemption. Infrastructure Insights Bottleneck analysis: HBM at 42% utilization, compute at 88% for Qwen3 training. Ecosystem Validation - HuggingFace integration: tested on Qwen3-1.7B. - W&B