Show HN: Auto GPU Kernel – Autonomous GPU-kernel discovery and optimizer
Auto GPU Kernel is an autonomous GPU-kernel discovery and optimizer that has achieved significant recognition. It ranked #1 in the MLSys 2026 FlashInfer AI Kernel Generation Contest, demonstrating an impressive average speedup of 34.93x. The tool is designed to work in isolated environments and can operate without a local GPU using cloud services.
- ▪Auto GPU Kernel ranked #1 in the MLSys 2026 FlashInfer AI Kernel Generation Contest.
- ▪It achieved an average speedup of 34.93x in the DeepSeek Sparse Attention track.
- ▪The kernel agent is compatible with FlashInfer format and can run on cloud services without a local GPU.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Dogacel/auto-gpu-kernel |
| Publication time | Tue, 26 May 2026 04:23:12 +0000 |
| Retrieval time | 2026-05-26T04:37:43.007Z |
| Last seen | 2026-05-26T04:37:43.007Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 9gyZWBxiFdYW |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Auto GPU Kernel 🏆 Autonomous GPU-kernel discovery & optimizer. Technical Report Ranked #1 on MLSys 2026 - FlashInfer AI Kernel Generation Contest for the DeepSeek Sparse Attention (DSA) track with an average speedup of 34.93x. Submissions can be found at: Kernel Runtime (ms) dsa_sparse_attention_h16_ckv512_kpe64_topk2048_ps64 — DSA Sparse Attention 0.010 dsa_topk_indexer_fp8_h64_d128_topk2048_ps64 — DSA TopK Indexer 0.016 Setup Copy the template directory into a separate folder / git repository to make sure your agents work in an isolated environment. The kernel agent is compatible with FlashInfer format and can run without a local GPU on cloud using Modal. Requires Claude Code CLI.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.