Donato Capitella
223 línte
9.2 KiB
Markdown
223 línte
9.2 KiB
Markdown
# AMD Strix Halo (gfx1151) — vLLM Toolbox/Container
|
||
|
||
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.
|
||
|
||
|
||
---
|
||
|
||
## 🚀 High-Performance Clustering Support (New!)
|
||
|
||
**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.
|
||
|
||
👉 **[Read the Full RDMA Cluster Setup Guide](rdma_cluster/setup_guide.md)** for hardware requirements and configuration instructions.
|
||
|
||
---
|
||
|
||
### 📦 Project Context
|
||
|
||
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.
|
||
|
||
### ❤️ Support
|
||
|
||
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! ☕
|
||
|
||
---
|
||
|
||
## Table of Contents
|
||
|
||
* [Tested Models (Benchmarks)](#tested-models-benchmarks)
|
||
* [1) Toolbx vs Docker/Podman](#1-toolbx-vs-dockerpodman)
|
||
* [2) Quickstart — Fedora Toolbx](#2-quickstart--fedora-toolbx)
|
||
* [3) Quickstart — Ubuntu (Distrobox)](#3-quickstart--ubuntu-distrobox)
|
||
* [4) Testing the API](#4-testing-the-api)
|
||
* [5) Use a Web UI for Chatting](#5-use-a-web-ui-for-chatting)
|
||
* [6) Host Configuration](#6-host-configuration)
|
||
* [7) Distributed Clustering (RDMA/RoCE)](#7-distributed-clustering-rdmaroce)
|
||
|
||
|
||
## Tested Models (Benchmarks)
|
||
|
||
View full benchmarks at: [https://kyuz0.github.io/amd-strix-halo-vllm-toolboxes/](https://kyuz0.github.io/amd-strix-halo-vllm-toolboxes/)
|
||
|
||
**Table Key:** Cell values represent `Max Context Length (GPU Memory Utilization)`.
|
||
|
||
| Model | TP | 1 Req | 4 Reqs | 8 Reqs | 16 Reqs |
|
||
| :--- | :--- | :--- | :--- | :--- | :--- |
|
||
| **`meta-llama/Meta-Llama-3.1-8B-Instruct`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
|
||
| **`google/gemma-3-12b-it`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
|
||
| **`openai/gpt-oss-20b`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
|
||
| **`Qwen/Qwen3-14B-AWQ`** | 1 | 40k (0.95) | 40k (0.95) | 40k (0.95) | 40k (0.95) |
|
||
| **`btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit`** | 1 | 256k (0.95) | 256k (0.95) | 256k (0.95) | 256k (0.95) |
|
||
| **`btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit`** | 1 | 256k (0.95) | 256k (0.95) | 256k (0.95) | 256k (0.95) |
|
||
| **`dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16`** | 1 | 256k (0.95) | 256k (0.95) | 256k (0.95) | 256k (0.95) |
|
||
| **`openai/gpt-oss-120b`** | 1 | 128k (0.95) | 128k (0.95) | 128k (0.95) | 128k (0.95) |
|
||
| **`zai-org/GLM-4.7-Flash`** | 1 | 198k (0.95) | 198k (0.95) | 198k (0.95) | 198k (0.95) |
|
||
|
||
|
||
---
|
||
|
||
## 1) Toolbx vs Docker/Podman
|
||
|
||
The `kyuz0/vllm-therock-gfx1151:latest` image can be used both as:
|
||
|
||
* **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.
|
||
* **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.
|
||
|
||
---
|
||
|
||
## 2) Quickstart — Fedora Toolbx
|
||
|
||
**Recommended:** Use the included `refresh_toolbox.sh` script. It pulls the latest image and creates the toolbox with the correct parameters:
|
||
|
||
```bash
|
||
./refresh_toolbox.sh
|
||
```
|
||
|
||
> **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.
|
||
|
||
**Manual Creation:**
|
||
|
||
To manually create a toolbox that exposes the GPU and relaxes seccomp:
|
||
|
||
```bash
|
||
toolbox create vllm \
|
||
--image docker.io/kyuz0/vllm-therock-gfx1151:latest \
|
||
-- --device /dev/dri --device /dev/kfd \
|
||
--group-add video --group-add render --security-opt seccomp=unconfined
|
||
```
|
||
|
||
Enter it:
|
||
|
||
```bash
|
||
toolbox enter vllm
|
||
```
|
||
|
||
**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.
|
||
|
||
### Serving a Model (Easiest Way)
|
||
|
||
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.
|
||
|
||
```bash
|
||
start-vllm
|
||
```
|
||
|
||
> **Cache note:** vLLM writes compiled kernels to `~/.cache/vllm/`.
|
||
|
||
---
|
||
|
||
## 3) Quickstart — Ubuntu (Distrobox)
|
||
|
||
Ubuntu’s toolbox package still breaks GPU access, so use Distrobox instead:
|
||
|
||
```bash
|
||
distrobox create -n vllm \
|
||
--image docker.io/kyuz0/vllm-therock-gfx1151:latest \
|
||
--additional-flags "--device /dev/kfd --device /dev/dri --group-add video --group-add render --security-opt seccomp=unconfined"
|
||
|
||
distrobox enter vllm
|
||
```
|
||
|
||
> **Verification:** Run `rocm-smi` to check GPU status.
|
||
|
||
### Serving a Model (Easiest Way)
|
||
|
||
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.
|
||
|
||
```bash
|
||
start-vllm
|
||
```
|
||
|
||
---
|
||
|
||
## 4) Testing the API
|
||
|
||
Once the server is up, hit the OpenAI‑compatible endpoint:
|
||
|
||
```bash
|
||
curl -X POST http://localhost:8000/v1/chat/completions \
|
||
-H "Content-Type: application/json" \
|
||
-d '{"model":"Qwen/Qwen2.5-7B-Instruct","messages":[{"role":"user","content":"Hello! Test the performance."}]}'
|
||
```
|
||
|
||
You should receive a JSON response with a `choices[0].message.content` reply.
|
||
|
||
If you don't want to bother specifying the model name, you can run this which will query the currently deployed model:
|
||
|
||
```bash
|
||
MODEL=$(curl -s http://localhost:8000/v1/models | jq -r '.data[0].id') curl -X POST http://localhost:8000/v1/chat/completions \
|
||
-H "Content-Type: application/json" \
|
||
-d "{
|
||
\"model\": \"$MODEL\",
|
||
\"messages\":[{\"role\":\"user\",\"content\":\"Hello! Test the performance.\"}]
|
||
}"
|
||
```
|
||
|
||
---
|
||
|
||
## 5) Use a Web UI for Chatting
|
||
|
||
If vLLM is on a remote server, expose port 8000 via SSH port forwarding:
|
||
|
||
```bash
|
||
ssh -L 0.0.0.0:8000:localhost:8000 <vllm-host>
|
||
```
|
||
|
||
Then, you can start HuggingFace ChatUI like this (on your host):
|
||
|
||
```bash
|
||
docker run -p 3000:3000 \
|
||
--add-host=host.docker.internal:host-gateway \
|
||
-e OPENAI_BASE_URL=http://host.docker.internal:8000/v1 \
|
||
-e OPENAI_API_KEY=dummy \
|
||
-v chat-ui-data:/data \
|
||
ghcr.io/huggingface/chat-ui-db
|
||
```
|
||
|
||
## 6) Host Configuration
|
||
|
||
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)
|
||
|
||
### 6.1 Test Configuration
|
||
|
||
| Component | Specification |
|
||
| :---------------- | :---------------------------------------------------------- |
|
||
| **Test Machine** | Framework Desktop |
|
||
| **CPU** | Ryzen AI MAX+ 395 "Strix Halo" |
|
||
| **System Memory** | 128 GB RAM |
|
||
| **GPU Memory** | 512 MB allocated in BIOS |
|
||
| **Host OS** | Fedora 43, Linux 6.18.5-200.fc43.x86_64 |
|
||
|
||
### 6.2 Kernel Parameters (tested on Fedora 42)
|
||
|
||
Add these boot parameters to enable unified memory while reserving a minimum of 4 GiB for the OS (max 124 GiB for iGPU):
|
||
|
||
iommu=pt amdgpu.gttsize=126976 ttm.pages_limit=32505856
|
||
|
||
| Parameter | Purpose |
|
||
|-----------------------------|--------------------------------------------------------------------------------------------|
|
||
| `iommu=pt` | Sets IOMMU to "Pass-Through" mode. This helps performance, reducing overhead for both the RDMA NIC and the iGPU unified memory access. |
|
||
| `amdgpu.gttsize=126976` | Caps GPU unified memory to 124 GiB; 126976 MiB ÷ 1024 = 124 GiB |
|
||
| `ttm.pages_limit=32505856` | Caps pinned memory to 124 GiB; 32505856 × 4 KiB = 126976 MiB = 124 GiB |
|
||
|
||
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
|
||
|
||
|
||
**Apply the changes:**
|
||
|
||
```
|
||
# Edit /etc/default/grub to add parameters to GRUB_CMDLINE_LINUX
|
||
sudo grub2-mkconfig -o /boot/grub2/grub.cfg
|
||
sudo reboot
|
||
```
|
||
|
||
## 7) Distributed Clustering (RDMA/RoCE)
|
||
|
||
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).
|
||
|
||
**Detailed Documentation:** [RDMA Cluster Setup Guide](rdma_cluster/setup_guide.md)
|
||
|
||
**Key Features:**
|
||
* **Custom RCCL Patch:** Use of a custom-built `librccl.so` to support RDMA on `gfx1151`.
|
||
* **Easy Setup:** `refresh_toolbox.sh` automatically detects and exposes RDMA devices.
|
||
* **Cluster Management:** Included `start-vllm-cluster` TUI for managing Ray and vLLM. |