diff --git a/.wordlist.txt b/.wordlist.txt index 5370f4752..8cc6399b6 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -156,6 +156,7 @@ GEMMs GFLOPS GFortran GFXIP +GGUF Gemma GiB GIM diff --git a/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst b/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst index fd1356d32..1ae246931 100644 --- a/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst +++ b/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst @@ -67,9 +67,14 @@ llama.cpp is also used in a range of real-world applications, including: - Various other AI applications use llama.cpp as their inference engine; for a detailed list, see the `user interfaces (UIs) section `__. -Refer to the `AMD ROCm blog `_, +For more use cases and recommendations, refer to the `AMD ROCm blog `__, where you can search for llama.cpp examples and best practices to optimize your workloads on AMD GPUs. +- The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration `__, + blog post outlines how the open-source llama.cpp framework enables efficient LLM inference—including interactive inference with ``llama-cli``, + server deployment with ``llama-server``, GGUF model preparation and quantization, performance benchmarking, and optimizations tailored for + AMD Instinct GPUs within the ROCm ecosystem. + .. _llama-cpp-docker-compat: Docker image compatibility diff --git a/docs/compatibility/ml-compatibility/ray-compatibility.rst b/docs/compatibility/ml-compatibility/ray-compatibility.rst index c5a2ed39f..2f5c83589 100644 --- a/docs/compatibility/ml-compatibility/ray-compatibility.rst +++ b/docs/compatibility/ml-compatibility/ray-compatibility.rst @@ -66,9 +66,15 @@ Use cases and recommendations GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance. +* The `Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows + `__ + blog post describes key use cases such as training and inference for large language models (LLMs), + model serving, hyperparameter tuning, reinforcement learning, and the orchestration of large-scale + workloads using Ray in the ROCm environment. + For more use cases and recommendations, see the AMD GPU tabs in the `Accelerator Support -topic `_ -of the Ray core documentation and refer to the `AMD ROCm blog `_, +topic `__ +of the Ray core documentation and refer to the `AMD ROCm blog `__, where you can search for Ray examples and best practices to optimize your workloads on AMD GPUs. .. _ray-docker-compat: