iree-org

iree

C++2020–2026

iree-org / iree

iree

A retargetable MLIR-based machine learning compiler and runtime toolkit.

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12Mentions

0Explicit recommenders

4KGitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

A retargetable MLIR-based machine learning compiler and runtime toolkit.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

openxla/stablehlo · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year12 dated mentions

Observed mentions: 2020-01-27 17:05:07 UTC to 2026-05-09 06:14:49 UTC.

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	2	2
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	1	1
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	7	7
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	0	0
2025	2025-01-01 00:00:00 UTC	2026-01-01 00:00:00 UTC	1	1
2026 (partial)	2026-01-01 00:00:00 UTC	2027-01-01 00:00:00 UTC	1	1

What Hacker News says

12 sources

tkocmathla·May 9, 2026

HEIR [1] is a homomorphic encryption compiler built on modern MLIR. IREE [2] is very actively developed ML compiler + runtime, also MLIR-based. [1] https://github.com/google/heir [2] https://github.com/iree-org/iree

In Mojo 1.0 Beta

sueszli·Dec 22, 2025

a heap-free implementation could be a really cool direction to explore. thanks! i think you might be interested in MLIR/IREE: https://github.com/openxla/iree

In Show HN: Autograd.c – A tiny ML framework built from scratch

PartiallyTyped·Nov 13, 2023

StableHLO[1] and IREE[2] are interesting projects that might help AMD here, from [1]: > Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE). From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend. [1] https://github.com/openxla/stablehlo [2] https://github.com/openxla/iree [3] https://www.tensorflow.org/xla

In Nvidia H200 Tensor Core GPU

PartiallyTyped·Nov 13, 2023

In Nvidia H200 Tensor Core GPU

iFire·Aug 9, 2023

Regarding NVIDIA domination, I want to promote that the Google project https://github.com/openxla/iree exists and IREE acts as a way to turn Tensorflow, Pytorch, and MLIR workflows to compute on cpu, vulkan compute, cuda, rocm, metal and others. https://github.com/RechieKho/IREE.gd -- RechieKho and I collaborate on making this work for Godot Engine, but IREE.gd is at a proof of concept stage.

In Nvidia reveals new A.I. chip, says costs of running LLMs will drop significantly

raphlinus·Aug 2, 2023

To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end. [1]: https://kompute.cc/ [2]: https://github.com/webonnx/wonnx [3]: https://github.com/openxla/iree

In VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library

lairv·Apr 25, 2023

I was going to answer the same, I find the approach of machine learning compilers that directly compile models to host and device code better than having to bring a huge runtime. There are exciting projects in this area like TVM Unity [1], IREE [2], or torch.export [3] [1] https://github.com/apache/tvm/tree/unity [2] https://github.com/openxla/iree [3] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...

In ONNX Runtime merges WebGPU backend

nl·Mar 17, 2023

Shouldn't it be possible to build a WebGL backend for OpenXLA? Edit: There seems to be some progress on a WASM backend for OpenXLA here: https://github.com/openxla/iree/issues/8327 and a proposed WebML working group at W3C: https://www.w3.org/2023/03/proposed-webmachinelearning-chart... that references OpenXLA

In Transformers.js

modeless·Jan 9, 2023

In the ML section you mentioned Kompute and MediaPipe. Have you seen IREE? It has a Vulkan-like compute-only HAL. https://github.com/iree-org/iree

In Requiem for piet-gpu-hal

noxa·Feb 23, 2022

Exactly the kind of things we've been talking about! A fun and challenging tradeoff space and it's always great to connect with others! Ahh linebender - I hadn't connected the name with your github account - piet-gpu is great, as is your blog! Also, for anyone skimming the comments this talk is fantastic and I share it with anyone new to the GPGPU space: https://www.youtube.com/watch?v=DZRn_jNZjbw We waffled a bit with the API granularity in the beginning and it's taken building out most of the rest of the project in order to nail it down (the big refactor still pending). The biggest issue is that in simple models we'll end up emitting a single command buffer but anything with control flow (that we can't predicate), data dependencies (sparsity, thresholding, etc), or CPU work in the middle (IO, custom user code, etc) can break that up. We

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also hit cases where we need to flush work - such as if we run out of usable memory and need to defragment or resize our pools. We want to be able to (but aren't yet) reuse command buffers (CUDA graphs, etc) and that requires being able to both cache them and recreate them on demand (if we resize a pool we have to invalidate all cached command buffers using those resources, as update-after-bind is not universally available and if shapes change there's big ripples). Since most models beyond simple vision ones are ~thousands of dispatches it also lets us better integrate into multithreaded applications like you mention as apps can record commands for themselves in parallel without synchronization. It still would be nice to have certain operations inlined, though, and for that we want to allow custom hooks that we call into to add commands to the command buffers, turning things inside-out to make small amounts of work like image transformations in-between model layers possible (I'm really hoping we can avoid modeling the entire graphics pipeline in the compiler and this would be a way around that :). We haven't yet started on scheduling across queues but that's also very interesting especially in multi-GPU cases (with x4/x8 GPUs being common in datacenters, or NUMA CPU clusters that can be scheduled similarly). We're fully open source (https://github.com/google/iree) but have been operating quietly while we get the groundwork in place - it's taken some time but now we're finally starting to stumble into success on certain problem categories (like transformers as in the post). Right now it's mostly just organized as a systems/compiler nerd honeypot for people looking for an ML/number crunching framework that (purposefully) doesn't look like any of the existing ones :) Would love to chat more - even if just to commiserate over GPU APIs and such - everyone is welcome on the discord where a bunch of us nerds have gathered or we could grab virtual coffee (realized just now that this hn acct is ancient - I'm benvanik@google.com :)

In PyTorch on Apple M1 MAX GPUs with SHARK – faster than TensorFlow-Metal

erichocean·Feb 27, 2020

Obviously you're entitled to your opinion, but I think it's completely wrong. (Note: I don't work for Google, or know anyone on the MLIR team.) Take a look at this recent talk by the people behind IREE[0] (who are not part of the MLIR team): https://drive.google.com/file/d/1os9FaPodPI59uj7JJI3aXnTzkut... In a nutshell, they have made HUGE gains by taking advantage of the MLIR infrastructure for a use case not anticipated by the MLIR authors (i.e. making the TensorFlow-to-hardware path easier to develop). In my opinion, MLIR is going to be much more successful long-term than LLVM is today, and by long-term, I mean "in the next few years." The number of teams consolidating already around MLIR is astounding, and having a multi-dialect SSA-based IR is a freaking game changer for compiler writers. [0] https://github.com/google/iree

In MLIR: A Compiler Infrastructure for the End of Moore's Law

Joky·Jan 27, 2020

I wouldn't be worried for MLIR. MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for example (https://github.com/google/iree ). The TensorFlow lite converter has been replaced by the new MLIR-based one, similarly for the Edge TPU. But more than that, what makes me confident is the traction we are getting outside Google. First, we landed MLIR in LLVM last month: https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80... The LLVM Fortran frontend (f18/flang) which will merge soon in the LLVM monorepo is using MLIR for their own IR. It'll be exiting to develop a non-ML MLIR-based frontend within LLVM! In particular Flang is opening an HPC perspective that could be leveraged by other DSLs later. They are adding an OpenMP dialect to MLIR right now: https://llvm.dis

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course.group/t/rfc-openmp-dialect-in-mlir/39... Intel has been actively porting their nGraph/PlaidML framework to be based on MLIR (search for "The Stripe dialect" and "nGraph Dialect" here: https://mlir.llvm.org/talks/ ). I'm less familiar with S4TF, but I know they recently got some nice new hires, including https://twitter.com/DaveAbrahams/status/1207690883782467584

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