209 lines
8.1 KiB
Markdown
209 lines
8.1 KiB
Markdown
# AMD Radeon 9700 AI PRO (gfx1201) — vLLM Toolbox/Container
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An **fedora-based** Docker/Podman container that is **Toolbx-compatible** (usable as a Fedora toolbox) for serving LLMs with **vLLM** on **AMD Radeon R9700 (gfx1201)**. Built on the TheRock nightly builds for ROCM.
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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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## Tested Models (Benchmarks)
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View full benchmarks at: [https://kyuz0.github.io/amd-r9700-vllm-toolboxes/](https://kyuz0.github.io/amd-r9700-vllm-toolboxes/)
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*Run benchmarks now include a comparison between the default Triton backend and the optional ROCm attention backend.*
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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 | 127k (0.98) | 127k (0.98) | 127k (0.98) | 127k (0.98) |
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| | 2 | 105k (0.98) | 105k (0.98) | 105k (0.98) | 105k (0.98) |
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| **`openai/gpt-oss-20b`** | 1 | 131k (0.98) | 131k (0.98) | 131k (0.98) | 131k (0.98) |
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| | 2 | 131k (0.95) | 131k (0.95) | 131k (0.95) | 131k (0.95) |
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| **`RedHatAI/Qwen3-14B-FP8-dynamic`** | 1 | 41k (0.98) | 41k (0.98) | 41k (0.98) | 41k (0.98) |
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| | 2 | 41k (0.95) | 41k (0.95) | 41k (0.95) | 41k (0.95) |
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| **`cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit`** | 1 | 151k (0.98) | 151k (0.98) | 151k (0.98) | 151k (0.98) |
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| | 2 | 262k (0.98) | 262k (0.98) | 262k (0.98) | 262k (0.98) |
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| **`cpatonn/Qwen3-Next-80B-A3B-Instruct-AWQ-4bit`** | 2 | 156k (0.98) | 156k (0.98) | 156k (0.98) | 156k (0.98) |
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| **`RedHatAI/gemma-3-12b-it-FP8-dynamic`** | 1 | 45k (0.98) | 45k (0.98) | 45k (0.98) | 45k (0.98) |
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| | 2 | 126k (0.98) | 126k (0.98) | 121k (0.95) | 121k (0.95) |
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| **`RedHatAI/gemma-3-27b-it-FP8-dynamic`** | 2 | 60k (0.98) | 60k (0.98) | 60k (0.98) | 60k (0.98) |
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### Advanced Tuning
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See [TUNING.md](TUNING.md) for a guide on how to enable undervolting and raise the power limit on AMD R9700 cards on Linux to improve performance and efficiency.
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### 🆕 Update: Comparison of Attention Backends (Triton vs ROCm)
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*Added Support for ROCm Native Attention Backend*
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I have added the ability to switch between the default **Triton** backend and the experimental **ROCm** native backend for attention operations. This provides you with more flexibility to optimize for stability or throughput depending on your specific model and workload.
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| Backend | Stability | Throughput | Compatibility |
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| :--- | :--- | :--- | :--- |
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| **Triton** (Default) | ✅ **High** | 🔸 Good | Works with all tested models |
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| **ROCm** | ⚠️ **Experimental** | 🚀 **Highest** | May fail with complex architectures |
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**Key Differences:**
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- **Triton**: The safe choice. It uses the Triton compiler to generate kernels and is the standard for vLLM on AMD.
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- **ROCm**: Uses composable kernel based attention. In my benchmarks, this often yields higher throughput (tokens/sec) but can be less stable, leading to crashes or "invalid graph" errors on some newer models.
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**How to Use:**
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1. **Easy Mode**: Select the backend in the `start-vllm` wizard (Item 5 in the menu).
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2. **Manual Mode**: Export the following environment variables before running `vllm serve`:
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```bash
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export VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1
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export VLLM_USE_TRITON_FLASH_ATTN=0
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```
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---
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## 1) Toolbx vs Docker/Podman
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The `kyuz0/vllm-therock-gfx1201: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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Create a toolbox that exposes the GPU and relaxes seccomp to avoid ROCm syscall issues:
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```bash
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toolbox create vllm-r9700 \
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--image docker.io/kyuz0/vllm-therock-gfx1201: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-r9700
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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 launch flags. It also allows you to select the experimental **ROCm attention backend**. This is the easiest way to get started.
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```bash
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# if your weights live on disk instead of on HuggingFace, point the
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# launcher at the directory that contains the model folders. the
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# script will look for a subdirectory matching the repo ID and use it
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# when launching.
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export LOCAL_MODEL_DIR=/workspace/models
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start-vllm
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# you can also just run the CLI yourself and pass the path directly:
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vllm serve /workspace/models/cpatonn/Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit ...
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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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### 离线/本地模式
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如果你希望完全禁用网络访问，只使用本地权重，可以利用 `LOCAL_MODEL_DIR`。
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脚本会在该目录下查找模型子目录；**找不到时会立即报错并退出**，
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不会尝试下载任何内容。适用于没有外网或受限环境的部署。
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```bash
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export LOCAL_MODEL_DIR=/workspace/models
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start-vllm # 本地模式，如果模型缺失则失败
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```
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### 离线/本地模式
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如果你希望完全禁用网络访问，只使用本地权重，可以利用 `LOCAL_MODEL_DIR`。
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脚本会在该目录下查找模型子目录；**找不到时会立即报错并退出**，
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不会尝试下载任何内容。适用于没有外网或受限环境的部署。
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```bash
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export LOCAL_MODEL_DIR=/workspace/models
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start-vllm # 本地模式，如果模型缺失则失败
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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-r9700 \
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--image docker.io/kyuz0/vllm-therock-gfx1201: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-r9700
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```
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> **Verification:** Run `rocm-smi` to check GPU status.
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### Serving a Model
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Same as above, you can use the **`start-vllm`** wizard to launch models easily.
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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":"Qwen3-Coder-30B-A3B-Instruct-GPTQ-4bit","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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