Maruthi Kunchala@mkunchala

ROCKET is a multi-agent system (Profiler → Planner → Implementer → Validator) that takes any PyTorch model, profiles it on AMD MI300X, hypothesizes which optimizations will help, applies them from a bounded toolbox of five transformations (dtype_cast, torch_compile, sdpa_attention, input_padding, kv_cache_config), validates output correctness, and measures the speedup — completely on its own. On a real AMD Instinct MI300X (ROCm 7) running Qwen2.5-7B-Instruct at batch=8, prompt 256 + new 512, ROCKET achieved a measured 2.93× throughput speedup: 62.59 → 183.47 tokens per second. The agent tried 5 tools and kept 1 (bf16 cast); correctly reverted the four that didn't beat the validator's 2% keep threshold — exactly the judgment a senior performance engineer would apply. How this is different from adjacent submissions: - ROCmPort AI translates CUDA → ROCm code. ROCKET makes the model FAST. - ReplayLab records GPU experiments. ROCKET acts on them. - Sakana's AI Scientist optimizes model accuracy. ROCKET optimizes throughput on AMD silicon; the question every AMD developer asks first that no one had automated.

Grab your ideas, spin up compute, and start building what’s next! Start with $100 in AMD Developer Cloud credits and begin building right away. Get access to hands-on tutorials, workshops, and curated learning resources to help you build faster and go further. To unlock credits and join the hackathon, sign up for the AMD AI Developer Program. 🤝 Go solo or team up — ship something real. 💰 $21,500+ total prize pool & 1 × AMD Radeon™ AI PRO R9700 GPU up for grabs. 📍 On-site Venue (May 9-10): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📍On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Developers, builders, and innovators - bring your boldest ideas and let’s turn them into real AI systems on AMD.