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FLAT: Flash Linear Attention on TPUs
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GITHUB · launched Oct 12, 2026 · 19 upvotes (top 60%)
- Horizontal
- Model & infra
- Developer
- AI-native
- Hobby / open-source project
Similar launches
See all 615 similar launches →Could you build this?
No Implementing Flash Linear Attention on Google Cloud TPUs requires expert knowledge of hardware tiling, Pallas/XLA kernel programming, and low-level TPU architecture.
What it would actually take: Building this requires writing custom JAX/Pallas kernels specifically mapped to TPU Matrix Multiply Units (MXUs) and Vector Units (VMUs). Developers must manually manage high-bandwidth memory (HBM) transfers, SRAM register allocation, and fused attention operators across TPU cores without relying on standard abstractions. This requires specialized deep learning compiler and hardware acceleration expertise.
Competitors
Other products that read as similar to this one : 615 launches clear the similarity bar, closest 8 shown.
Attention rank: #364 of 616 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 346 days after the earliest competitor.
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- KronLSTM · github · 2026-10-19 · 47 upvotes · similarity 0.54
- FlashMask · github · 2026-10-14 · 35 upvotes · similarity 0.53
- Vehluna · hn · 2026-01-11 · 5 upvotes · similarity 0.52
- DASA · github · 2026-10-13 · 29 upvotes · similarity 0.52
- Simple-Attention-Sparsification · github · 2026-09-10 · 48 upvotes · similarity 0.51
- DeepSelect · github · 2026-09-09 · 327 upvotes · similarity 0.51
- glance-speedlab · github · 2026-09-23 · 48 upvotes · similarity 0.50
Other launches for this product
More details
- External ID
- 1416244741
- Company
- n/a
- Product
- At 16K features, flat autoencoders break. Curved space doesn't
- Website domain
- github.com
- Cohort
- n/a
- Upvotes percentile
- 0.40
- All tags
- n/a
- AI summary
- flash linear attention implementation for tpus
- Manually corrected
- No