Chisel – GPU development through MCP
Chisel – GPU development through MCP
We've been running lots of experiments on AMD MI300Xs. The price/performance is compelling compared to NVIDIA, and ROCm is finally usable. But, no local hardware means constant SSH juggling. Upload code, compile remotely, run rocprof, download results, repeat. We were spending more time managing infrastructure than optimizing kernels. We're currently competing in the GPU mode kernel optimization competition. Our goal is to build the fastest AMD kernels in the world. We spend a good chunk of our time setting up the infra to profile these kernels. So we built Chisel internally to make GPU development feel local to us. One command spins up a droplet, syncs your code, runs profiling, and pulls results back. It handles the SSH, rsync, and teardown automatically. The profiling integration was the killer feature for us. Since building Chisel, I have Claude constantly running experiments seamlessly and in parallel. I feel so much more productive when exploring different optimization strategies for these kernels. Available on PyPI: pip install chisel-cli Source: https://github.com/Herdora/chisel Would love feedback and thoughts from the community, especially from people doing GPU work and exploring AMD alternatives to NVIDIA. If you're interested in contributing, we'd welcome any help in making chisel better.
Share cardActual performance
Launch Intel predictions
Analyze your own launch →Incorrect prediction on native model
Similar products
Gource visualizations rendered without a GPU
Lambda Echelon GPU Cluster
GPU PaaS
Slurmq – GPU quota enforcement for Slurm
GPU Accelerated PDAL
WebSocket-first development
Caddy 2 Development Branch
ChatFor.Us (very much in development)
Compiler development
Dockpit – Painless isolation for you microservice development