# \[ANN\] cuTile.jl v0.3 + webinar

**URL:** https://discourse.julialang.org/t/ann-cutile-jl-v0-3-webinar/136988
**Category:** Package Announcements
**Tags:** gpu, cuda
**Created:** [May 5, 2026, 2:04pm UTC](https://discourse.julialang.org/t/ann-cutile-jl-v0-3-webinar/136988 "2026-05-05T14:04:03Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![maleadt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maleadt/32/10097_2.png) [@maleadt](https://discourse.julialang.org/u/maleadt)
#### Post date: [May 5, 2026, 2:04pm UTC](https://discourse.julialang.org/t/ann-cutile-jl-v0-3-webinar/136988/1 "2026-05-05T14:04:03Z")

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I’ve just tagged cuTile.jl v0.3, featuring:

- **CUDA.jl integration.** Launching a cuTile kernel is now just `@cuda backend=cuTile ...`.
- **Better performance.** We now match or outperform NVIDIA’s cuTile Python on every benchmark we ship.
- **Much improved latency** , with TTFX the same as with regular CUDA.jl kernels (~1.8s for a trivial kernel on my system).
- **Random number generation** , both host-level and in-kernel. Performance matches or beats cuRAND and the new GPUArrays.jl’ generator.
- **Array slicing.** `@view A[i:j, :]` now produces a sub-range TileArray you can pass to `ct.load` / `ct.store`.

Full write-up with code samples and benchmark numbers can be found on [juliagpu.org](http://juliagpu.org): [cuTile.jl 0.3: CUDA.jl integration, and even better performance & latency ⋅ JuliaGPU](https://juliagpu.org/post/2026-05-05-cutile_0.3/)

## Upcoming webinar

If you’d like a guided tour, Andy Terrel (NVIDIA) and I are running a joint webinar on May 12, 2026 at 1 PM ET covering CUDA Tile’s design, how cuTile.jl is built on top of it, and several worked examples. Sign up here: [cuTile.jl for High-Performance Computing in Julia - Event - JuliaHub](https://juliahub.com/events/cutile.jl-for-high-performance-computing-in-julia)