lablab-ai-amd-developer-hackathon/ROCKET-mi300x

⚡ ROCKET

An AI scientist whose only research domain is making models faster on AMD MI300X.

ROCKET is a multi-agent system that takes a PyTorch model, profiles it on AMD MI300X, hypothesizes which optimizations will help, applies them, validates correctness, and measures the speedup — completely on its own. Output: a measured speedup, a research log, and a PR-ready diff.

Real result on real hardware. Qwen2.5-7B-Instruct on AMD Instinct MI300X (ROCm 7), batch=8, prompt 256 + new 512: baseline 62.59 tok/s → final 183.47 tok/s. 2.93× honest speedup. The agent tried 5 tools and kept 1 (bf16 cast); rejected the 4 that didn't beat the validation threshold.

Built solo in 24 hours for the AMD x lablab.ai Developer Hackathon (May 2026).

What makes ROCKET different

The hackathon is full of great agentic systems — for medical triage, code translation, GPU debugging. ROCKET targets a different problem: "how do I make this model faster on AMD?" is the first question every developer asks, and ROCKET answers it autonomously.

• —ROCmPort AI translates CUDA code → ROCm code.
• —ReplayLab records GPU experiments and recovers from failures.
• —ROCKET makes the model fast. Different verb, different value.

Architecture

                ┌──────────────┐
       ┌──────▶│  Profiler    │  torch.profiler / rocprof
       │       └──────┬───────┘
       │              │  hotspot summary
       │              ▼
       │       ┌──────────────┐
       │       │  Planner     │  Qwen2.5-7B-Instruct on MI300X
       │       └──────┬───────┘  (vLLM endpoint)
       │              │  picks tool from bounded toolbox
       │              ▼
       │       ┌──────────────┐
       │       │  Implementer │  applies one of 5 transformations
       │       └──────┬───────┘
       │              │
       │              ▼
       │       ┌──────────────┐
       └───────│  Validator   │  re-bench + correctness check
               └──────────────┘  keep if speedup ≥ threshold

The bounded toolbox

ROCKET doesn't write arbitrary code. The agent picks from a curated set of high-leverage transformations:

The bounded search space is the point: the agent's job is which transformation to try, in what order, with which params, given the profile.

Tech stack

• —Hardware: AMD Instinct MI300X (192 GB HBM3) via AMD Developer Cloud
• —Runtime: ROCm 7.0 + PyTorch 2.6.0
• —Planner brain: Qwen2.5-7B-Instruct (served via vLLM on the same MI300X)
• —Target model: Qwen2.5-1.5B-Instruct (dev) / Qwen2.5-7B-Instruct (demo)
• —Frontend: Streamlit (this Space)
• —Profiling: torch.profiler

Run it

# On an MI300X droplet (AMD Developer Cloud, ROCm 7.0 + PyTorch 2.6.0 image)
git clone <repo>
cd rocket
pip install -r requirements.txt

# Start a local Qwen vLLM server (the planner brain)
vllm serve Qwen/Qwen2.5-7B-Instruct --port 8000 &

# Run the orchestrator
python -m rocket.orchestrator --model Qwen/Qwen2.5-1.5B-Instruct --iterations 5

The trace is written to logs/run.jsonl and is what powers this Space's replay view.

What's in this Space

This is a replay of an actual ROCKET run on AMD MI300X. HF Space free-tier doesn't have MI300X, so the agent ran on the droplet, the trace is shipped here, and the Space animates the journey: live tok/s chart, agent reasoning, decision log.

If you'd like to like this Space ❤️, that helps with the HF community prize at the hackathon.

Built by

A solo team, hacking through the night for the AMD Developer Hackathon. Repo and team page on lablab.ai.