Chisel – Profile GPU Kernels Without a GPU (Nvidia and AMD)

Hacker News

We built Chisel to make GPU kernel profiling hardware-free. It lets you run chisel profile kernel.cu and get full Nsight/Ncompute or rocprofv3 reports without a GPU needed. It spins up remote H100, L40S, or MI300X machines (via DigitalOcean for now, but gonna expand backends soon), runs your code, and gives you back detailed traces (kernel timings, memory transfers, API calls, etc). Everything is CLI-based and designed for iterative dev—profiling takes \~1–2 minutes per run. For example: # Profile a PyTorch training script on H100 with Nsight Systems chisel profile --nsys train.py # Profile a HIP kernel on MI300X with system trace chisel profile --rocprofv3="--sys-trace" matrix_add.cpp Repo: https://github.com/Herdora/chisel PyPI: pip install chisel-cli Would love feedback! especially from anyone building custom kernels, ML layers, or low-level GPU ops.

Visit product View on Hacker NewsJun 26, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, code · Missing: agents, macos, agent

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, calls · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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