Update blog

Author: cmdr2

content/blog/2025-10-10-1760088945.md

@@ -13,8 +13,8 @@ Some notes on machine-learning compilers, gathered while researching tech for Ea
 ## tl;dr summary
 
 The current state is:
-1. Vendor-specific compilers are the only performant options on consumer GPUs. For e.g. [TensorRT-RTX](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/index.html) for NVIDIA, [MiGraphX](https://rocm.docs.amd.com/projects/AMDMIGraphX/en/latest/) for AMD, [OpenVINO](https://github.com/openvinotoolkit/openvino) for Intel.
-2. Cross-vendor compilers are just not performant enough for Stable Diffusion-class workloads on consumer GPUs. For e.g. like [TVM](https://tvm.apache.org/), [IREE](https://iree.dev/), [XLA](https://openxla.org/xla).
+1. Vendor-specific compilers are the only performant options on consumer GPUs right now. For e.g. [TensorRT-RTX](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/index.html) for NVIDIA, [MiGraphX](https://rocm.docs.amd.com/projects/AMDMIGraphX/en/latest/) for AMD, [OpenVINO](https://github.com/openvinotoolkit/openvino) for Intel.
+2. Cross-vendor compilers are just not performant enough right now for Stable Diffusion-class workloads on consumer GPUs. For e.g. like [TVM](https://tvm.apache.org/), [IREE](https://iree.dev/), [XLA](https://openxla.org/xla).
 
 The focus of cross-vendor compilers seems to be either on datacenter hardware, or embedded devices. The performance on desktops and laptops is pretty poor. Mojo doesn't target this category (and doesn't support Windows). Probably because datacenters and embedded devices are currently where the attention (and money) is.