grammar

Author: adamlam2-amd

playbooks/supplemental/clustering-rccl/README.md

@@ -13,15 +13,15 @@ SPDX-License-Identifier: MIT
 
 ## Overview
 
-Your Ryzen™ AI Halo™ is already capable of running large language models locally. Clustering takes this further by combining the GPU memory of multiple systems over a local network, giving you access to even larger models with stronger reasoning, better code generation, and deeper multilingual understanding, all entirely on your own hardware.
+Your Ryzen™ AI Halo is already capable of running large language models locally. Clustering takes this further by combining the GPU memory of multiple systems over a local network, giving you access to even larger models with stronger reasoning, better code generation, and deeper multilingual understanding, all entirely on your own hardware.
 
-This playbook teaches you how to cluster two Ryzen™ AI Halo™ systems using RCCL (ROCm Communication Collectives Library) with vLLM and run Qwen3.5-397B, a 397B parameter model, across both machines with ROCm acceleration.
+This playbook teaches you how to cluster two Ryzen™ AI Halo systems using RCCL (ROCm Communication Collectives Library) with vLLM and run Qwen3.5-397B, a 397B parameter model, across both machines with ROCm acceleration.
 
 ## What You'll Learn
 
-- How to extend VRAM allocation on Ryzen™ AI Halo™ systems
+- How to extend VRAM allocation on Ryzen™ AI Halo systems
 - Launching vLLM with ROCm support
-- Configuring RCCL for multi-node tensor-parallel inference across two Ryzen™ AI Halo™ systems
+- Configuring RCCL for multi-node tensor-parallel inference across two Ryzen™ AI Halo systems
 - Running a 397B parameter model across two networked Ryzen™ AI Halo systems
 
 ## Prerequisites
@@ -92,7 +92,7 @@ You should see a speed of `10000Mb/s`:
 
 On Linux, ROCm utilizes a shared system memory pool, and this pool is configured by default to half the system memory.
 
-This amount can be increased by changing the kernel's Translation Table Manager (TTM) page setting, with the following instructions. AMD recommends setting the minimum dedicated VRAM in the BIOS (0.5GB)
+This amount can be increased by changing the kernel's Translation Table Manager (TTM) page setting, with the following instructions. AMD recommends setting the minimum dedicated VRAM in the BIOS (0.5 GB).
 
 * Install the pipx utility and add the path for pipx installed wheels into the system search path.
 
@@ -101,7 +101,7 @@ This amount can be increased by changing the kernel's Translation Table Manager
   pipx ensurepath
   ```
 
-* Install the amd-debug-tools wheel from PyPi.
+* Install the amd-debug-tools wheel from PyPI.
   ```bash
   pipx install amd-debug-tools
   ```
@@ -224,12 +224,12 @@ To connect Open WebUI to your vLLM endpoint:
 
 ![Open WebUI connection settings for the vLLM endpoint](assets/openwebui-connection.png)
 
-Once connected, select the model from the model dropdown in Open WebUI and start chatting. The model is now running across both of your Ryzen™ AI Halo™ nodes:
+Once connected, select the model from the model dropdown in Open WebUI and start chatting. The model is now running across both of your Ryzen™ AI Halo nodes:
 
 ![Chatting with Qwen3.5-397B in Open WebUI](assets/openwebui-chat.png)
 
 ## Next Steps
 
 - **Explore other models**: Discover new models on [Hugging Face](https://huggingface.co/models?&sort=trending) that fit within your cluster's combined GPU memory
-- **Scale to four nodes**: Add two more Ryzen™ AI Halo™ systems as additional Ray workers to shard models across even more GPUs. This requires an Ethernet switch with at least four ports, one for each node. Follow [Step 2: Join the Cluster](#step-2-join-the-cluster-machine-2) on each additional worker and increase `--tensor-parallel-size` accordingly
+- **Scale to four nodes**: Add two more Ryzen™ AI Halo systems as additional Ray workers to shard models across even more GPUs. This requires an Ethernet switch with at least four ports, one for each node. Follow [Step 2: Join the Cluster](#step-2-join-the-cluster-machine-2) on each additional worker and increase `--tensor-parallel-size` accordingly
 - **Try other parallelism strategies**: vLLM supports [expert parallel](https://docs.vllm.ai/en/latest/serving/expert_parallel_deployment/) for mixture-of-experts models and [data parallel](https://docs.vllm.ai/en/latest/serving/data_parallel_deployment/) for higher throughput. Experiment with `--enable-expert-parallel` and `--data-parallel-size` to find the best configuration for your workload