An independent developer known as Speedstu built a PowerShell-based toolkit that combines ZLUDA with AMD's HIP/ROCm SDK, allowing CUDA-only applications to run on AMD GPUs in Windows without dual-booting or virtual machines. The setup successfully mapped CUDA's driver API and libraries like cuBLAS, cuSPARSE, and cuFFT to AMD equivalents, and was tested by training a small reinforcement-learning model on a Radeon RX 9060 XT using unmodified CUDA code.
tomshardware.com
· 2026-09-14
Developers behind the ZLUDA project have released a reproducible stack combining ZLUDA v6-preview.69 with AMD's HIP SDK 6.4, allowing CUDA-targeted Windows applications, including LibTorch-based machine learning workloads, to run on AMD hardware. The setup has been validated specifically on the Radeon RX 9060 XT (gfx1200), where core CUDA libraries like cuBLAS and cuFFT passed checks and a PPO reinforcement learning network completed training over 65,536 timesteps.
github.com
· 2026-09-13
The vLLM project released version 0.28.0, combining 584 commits from 270 contributors. The update centers on deep performance work for Kimi-K3, including new decode context parallelism, fused kernels, memory-saving expert sharding, and ROCm support, alongside DeepSeek V4 improvements such as end-to-end sparse MLA and AMD Quark NVFP4 support.
github.com
· 2026-08-29
GoKawiil