rdna4

THE FIRST DLSS 5 MANAGER

Zig INferenCe Engine — Local LLM inference on AMD GPUs and Apple Silicon

Windows-only version of ComfyUI which uses AMD's official ROCm and PyTorch libraries to get better performance with AMD GPUs. [auto-installation and popular performance enhancing packages like triton * sage-attention * flash-attention * bitsandbytes included ]

A set of performant RDNA/HIP patches against llama.cpp focusing on high speed inferencing with numerics assurance

The intelligent OptiScaler installer Linux gamers needed. Automates FSR4, XeSS & DLSS configuration with GPU-optimized profiles for RDNA3/4, Arc & RTX cards.

Shape-specialized ROCm inference for Qwen3.8 Flash Next on RDNA4

Utilities for accessing AMD's Machine-Readable GPU ISA Specifications.

Windows tool to bypass AMD driver clock gating and unlock real GPU boost clocks (RDNA4)

LLM inference engine for AMD RDNA4 — Rust + Vulkan compute shaders, gguf & native FP8.

WHIRL (Windows HIP Inference for RDNA LLMs): native Windows C++/HIP LLM inference engine for the AMD Radeon AI PRO R9700

Measured llama.cpp benchmarks on AMD Radeon RDNA4 with ROCm: RX 9070 XT + Radeon AI PRO R9700 (48 GB). Qwen3.8, Gemma 4, GLM, gpt-oss; GGUF quant quality (KLD), MTP speculative decoding, 128K context, Claude Code with local models.

AMD Radeon 9700 AI PRO RDNA multi GPU Tools for llm/vLLM inference, tuning, overclocking, undervolting and LLM models benchmarking

vLLM for AMD RDNA4 (gfx1201): Radeon AI PRO R9700 & RX 9070 XT — native MXFP4 linear kernel + spec-decode verify fix, carried as rebased branches (fork-carry model)

让 MinerU 在 AMD 显卡上以 vLLM + hybrid-auto-engine 满血运行！ 本项目是对官方文档中 AMD/ROCm 生态支持的有效补充。针对 MinerU 3.x 提供了一套完整的 ROCm 7.x + PyTorch 2.11.0 + vLLM 源码编译与部署方案，解析质量与速度对标 NVIDIA 旗舰显卡。

Run Gemma-4-31B at full 256K context on a $1,400 AMD RDNA4 GPU (gfx1201): TurboQuant KV cache + HIP-graph-safe Flash-Attention for llama.cpp, fully measured on real hardware.

Quantized attention for AMD RX 9070 / 9070 XT (RDNA4, gfx1201): a hand-written HIP attention kernel behind SageAttention, serving the fused q/k/v views video models send, at 0.87-0.94x PR #368's time over production sizes.

llama.cpp with native AMD RDNA4 (gfx1201) ROCm 7.11 support - 98.97 tok/s AI inference, competitive with RTX 4070 Ti, 32GB VRAM

Moved. See: https://github.com/RX4Darwin/AMDRadeonRDNA4.kext

Hardware-focused llama.cpp fork for Windows and dual AMD RDNA4 RX 9070 XT GPUs: ROCm/HIP + Vulkan backends, PyQt6 GUI (RDNA LLM Studio), MTP speculative decoding, FP8 attention, long-context Qwen3.8 benchmarks

The unmodified upstream Linux amdgpu + amdkfd driver running on macOS: AMD Radeon GPUs over Thunderbolt on Apple Silicon, inside a DriverKit extension.