Donato Capitella
223 rader
9.2 KiB
Markdown
223 rader
9.2 KiB
Markdown
# AMD Strix Halo (gfx1151) — vLLM Toolbox/Container
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An **Fedora 43** Docker/Podman container that is **Toolbx-compatible** (usable as a Fedora toolbox) for serving LLMs with **vLLM** on **AMD Ryzen AI Max “Strix Halo” (gfx1151)**. Built on the **TheRock nightly builds** for ROCm.
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---
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## 🚀 High-Performance Clustering Support (New!)
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**Update:** This toolbox now ships with a **custom build of ROCm/RCCL** that enables **native RDMA/RoCE v2 support for Strix Halo (gfx1151)**. This allows you to connect two nodes via a low-latency interconnect (e.g., Intel E810) and run vLLM with Tensor Parallelism (TP=2) effectively acting as a single 256GB Unified Memory GPU.
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👉 **[Read the Full RDMA Cluster Setup Guide](rdma_cluster/setup_guide.md)** for hardware requirements and configuration instructions.
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---
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### 📦 Project Context
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This repository is part of the **[Strix Halo AI Toolboxes](https://strix-halo-toolboxes.com)** project. Check out the website for an overview of all toolboxes, tutorials, and host configuration guides.
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### ❤️ Support
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This is a hobby project maintained in my spare time. If you find these toolboxes and tutorials useful, you can **[buy me a coffee](https://buymeacoffee.com/dcapitella)** to support the work! ☕
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---
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## Table of Contents
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* [Tested Models (Benchmarks)](#tested-models-benchmarks)
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* [1) Toolbx vs Docker/Podman](#1-toolbx-vs-dockerpodman)
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* [2) Quickstart — Fedora Toolbx](#2-quickstart--fedora-toolbx)
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* [3) Quickstart — Ubuntu (Distrobox)](#3-quickstart--ubuntu-distrobox)
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* [4) Testing the API](#4-testing-the-api)
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* [5) Use a Web UI for Chatting](#5-use-a-web-ui-for-chatting)
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* [6) Host Configuration](#6-host-configuration)
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* [7) Distributed Clustering (RDMA/RoCE)](#7-distributed-clustering-rdmaroce)
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## Tested Models (Benchmarks)
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View full benchmarks at: [https://kyuz0.github.io/amd-strix-halo-vllm-toolboxes/](https://kyuz0.github.io/amd-strix-halo-vllm-toolboxes/)
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**Table Key:** Cell values represent `Max Context Length (GPU Memory Utilization)`.
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| Model | TP | 1 Req | 4 Reqs | 8 Reqs | 16 Reqs |
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| :--- | :--- | :--- | :--- | :--- | :--- |
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| **`meta-llama/Meta-Llama-3.1-8B-Instruct`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
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| **`google/gemma-3-12b-it`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
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| **`openai/gpt-oss-20b`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
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| **`Qwen/Qwen3-14B-AWQ`** | 1 | 40k (0.95) | 40k (0.95) | 40k (0.95) | 40k (0.95) |
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| **`btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit`** | 1 | 256k (0.95) | 256k (0.95) | 256k (0.95) | 256k (0.95) |
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| **`btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit`** | 1 | 256k (0.95) | 256k (0.95) | 256k (0.95) | 256k (0.95) |
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| **`dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16`** | 1 | 256k (0.95) | 256k (0.95) | 256k (0.95) | 256k (0.95) |
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| **`openai/gpt-oss-120b`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
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| **`zai-org/GLM-4.7-Flash`** | 1 | 198k (0.95) | 198k (0.95) | 198k (0.95) | 198k (0.95) |
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---
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## 1) Toolbx vs Docker/Podman
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The `kyuz0/vllm-therock-gfx1151:latest` image can be used both as:
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* **Fedora Toolbx (recommended for development):** Toolbx shares your **HOME** and user, so models/configs live on the host. Great for iterating quickly while keeping the host clean.
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* **Docker/Podman (recommended for deployment/perf):** Use for running vLLM as a service (host networking, IPC tuning, etc.). Always **mount a host directory** for model weights so they stay outside the container.
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---
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## 2) Quickstart — Fedora Toolbx
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**Recommended:** Use the included `refresh_toolbox.sh` script. It pulls the latest image and creates the toolbox with the correct parameters:
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```bash
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./refresh_toolbox.sh
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```
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> **InfiniBand / RDMA Support:** The script automatically detects if a fast InfiniBand link is active (checks `/dev/infiniband`). If found, it correctly sets up the container to expose these devices, enabling high-performance clustering.
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**Manual Creation:**
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To manually create a toolbox that exposes the GPU and relaxes seccomp:
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```bash
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toolbox create vllm \
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--image docker.io/kyuz0/vllm-therock-gfx1151:latest \
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-- --device /dev/dri --device /dev/kfd \
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--group-add video --group-add render --security-opt seccomp=unconfined
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```
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Enter it:
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```bash
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toolbox enter vllm
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```
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**Model storage:** Models are downloaded to `~/.cache/huggingface` by default. This directory is shared with the host if you created the toolbox correctly, so downloads persist.
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### Serving a Model (Easiest Way)
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The toolbox includes a TUI wizard called **`start-vllm`** which includes pre-configured models and handles the launch flags for you. This is the easiest way to get started.
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```bash
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start-vllm
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```
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> **Cache note:** vLLM writes compiled kernels to `~/.cache/vllm/`.
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---
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## 3) Quickstart — Ubuntu (Distrobox)
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Ubuntu’s toolbox package still breaks GPU access, so use Distrobox instead:
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```bash
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distrobox create -n vllm \
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--image docker.io/kyuz0/vllm-therock-gfx1151:latest \
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--additional-flags "--device /dev/kfd --device /dev/dri --group-add video --group-add render --security-opt seccomp=unconfined"
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distrobox enter vllm
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```
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> **Verification:** Run `rocm-smi` to check GPU status.
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### Serving a Model (Easiest Way)
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The toolbox includes a TUI wizard called **`start-vllm`** which includes pre-configured models and handles the launch flags for you. This is the easiest way to get started.
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```bash
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start-vllm
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```
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---
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## 4) Testing the API
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Once the server is up, hit the OpenAI‑compatible endpoint:
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```bash
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curl -X POST http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{"model":"Qwen/Qwen2.5-7B-Instruct","messages":[{"role":"user","content":"Hello! Test the performance."}]}'
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```
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You should receive a JSON response with a `choices[0].message.content` reply.
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If you don't want to bother specifying the model name, you can run this which will query the currently deployed model:
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```bash
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MODEL=$(curl -s http://localhost:8000/v1/models | jq -r '.data[0].id') curl -X POST http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d "{
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\"model\": \"$MODEL\",
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\"messages\":[{\"role\":\"user\",\"content\":\"Hello! Test the performance.\"}]
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}"
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```
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---
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## 5) Use a Web UI for Chatting
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If vLLM is on a remote server, expose port 8000 via SSH port forwarding:
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```bash
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ssh -L 0.0.0.0:8000:localhost:8000 <vllm-host>
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```
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Then, you can start HuggingFace ChatUI like this (on your host):
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```bash
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docker run -p 3000:3000 \
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--add-host=host.docker.internal:host-gateway \
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-e OPENAI_BASE_URL=http://host.docker.internal:8000/v1 \
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-e OPENAI_API_KEY=dummy \
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-v chat-ui-data:/data \
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ghcr.io/huggingface/chat-ui-db
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```
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## 6) Host Configuration
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This should work on any Strix Halo. For a complete list of available hardware, see: [Strix Halo Hardware Database](https://strixhalo-homelab.d7.wtf/Hardware)
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### 6.1 Test Configuration
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| Component | Specification |
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| :---------------- | :---------------------------------------------------------- |
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| **Test Machine** | Framework Desktop |
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| **CPU** | Ryzen AI MAX+ 395 "Strix Halo" |
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| **System Memory** | 128 GB RAM |
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| **GPU Memory** | 512 MB allocated in BIOS |
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| **Host OS** | Fedora 43, Linux 6.18.5-200.fc43.x86_64 |
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### 6.2 Kernel Parameters (tested on Fedora 42)
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Add these boot parameters to enable unified memory while reserving a minimum of 4 GiB for the OS (max 124 GiB for iGPU):
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iommu=pt amdgpu.gttsize=126976 ttm.pages_limit=32505856
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| Parameter | Purpose |
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|-----------------------------|--------------------------------------------------------------------------------------------|
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| `iommu=pt` | Sets IOMMU to "Pass-Through" mode. This helps performance, reducing overhead for both the RDMA NIC and the iGPU unified memory access. |
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| `amdgpu.gttsize=126976` | Caps GPU unified memory to 124 GiB; 126976 MiB ÷ 1024 = 124 GiB |
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| `ttm.pages_limit=32505856` | Caps pinned memory to 124 GiB; 32505856 × 4 KiB = 126976 MiB = 124 GiB |
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Source: https://www.reddit.com/r/LocalLLaMA/comments/1m9wcdc/comment/n5gf53d/?context=3&utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
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**Apply the changes:**
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```
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# Edit /etc/default/grub to add parameters to GRUB_CMDLINE_LINUX
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sudo grub2-mkconfig -o /boot/grub2/grub.cfg
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sudo reboot
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```
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## 7) Distributed Clustering (RDMA/RoCE)
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This toolbox supports high-performance clustering of multiple Strix Halo nodes using Infiniband or RoCE v2 (e.g., Intel E810). This enables **Tensor Parallelism** across machines with extremely low latency (~5µs).
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**Detailed Documentation:** [RDMA Cluster Setup Guide](rdma_cluster/setup_guide.md)
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**Key Features:**
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* **Custom RCCL Patch:** Use of a custom-built `librccl.so` to support RDMA on `gfx1151`.
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* **Easy Setup:** `refresh_toolbox.sh` automatically detects and exposes RDMA devices.
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* **Cluster Management:** Included `start-vllm-cluster` TUI for managing Ray and vLLM. |