AMD v620 Solutions Library: ROCm Build System for Inference+Training on RDNA2/GFX1030

Overview

v620 Solutions Library is a containerized CI/CD build system that compiles vLLM v0.25.x, llama.cpp, and HipFire from source against ROCm 7.2.x for AMD Radeon PRO V620 and RDNA2 GPUS (gfx1030 target). Currently, two seperate images are published to the GitHub Container Registry on every commit:

v620/rocm-base – ROCm 7.2.x + build toolchain + Bun/Rust
v620/vllm-gfx1030 – Compiled vLLM + PyTorch + Triton for RDNA2 inference

Hardware Target Specification

Specification	Value
Architecture	AMD RDNA2 (Recurrent DNA 2nd Generation)
Target GPU	Radeon PRO V620, RX 6800/6900 XT
GFX ISA	gfx1030 (Wave64)
CU Count	72
Stream Processors	4,608
Memory	32 GB GDDR6 ECC @ 16 Gbps
Memory Bandwidth	512 GB/s
Peak FP32	20.28 TFLOPS
Peak FP16	40.55 TFLOPS
Virtualization	SR-IOV (4 vGPU partition support)

Repository Structure

v620/
├── Dockerfile.base              # ROCm 7.2.x + build environment
├── vllm/
│   └── Dockerfile               # Multi-stage vLLM build
└── .github/workflows/
    ├── build-base.yml           # Build & push rocm-base image
    └── build-vllm.yml           # Build & push vllm-gfx1030 image

Build Artifacts & Container Pipeline

Image Build Dependency Graph

rocm/dev-ubuntu-24.04:7.2.x-complete (public AMD ROCm base)
         ↓
    Dockerfile.base
         ↓
  v620/rocm-base:latest (ghcr.io/sar/v620/rocm-base)
         ↓
   vllm/Dockerfile (uses BASE_IMAGE arg)
         ↓
 v620/vllm-gfx1030:latest (ghcr.io/sar/v620/vllm-gfx1030)

Dockerfile.base Specification

Base Image: rocm/dev-ubuntu-24.04:7.2.x-complete
Purpose: Foundation layer with ROCm runtime, compilers, and system dependencies

Installed Components

System Packages:
  • build-essential (gcc/g++/make)
  • cmake >= 3.26.1 (LLVM/vLLM build)
  • ninja-build (parallel build acceleration)
  • python3.12 + dev headers (runtime)
  • rocm-cmake (AMD-specific CMake modules)
  • git, curl, wget, pkg-config, libssl-dev

Languages & Package Managers:
  • Bun (JavaScript/TypeScript runtime)
  • Rust (installed via rustup)

Environment Variables:
  • HSA_OVERRIDE_GFX_VERSION=10.3.0 (force RDNA2 detection)
  • AMDGPU_TARGETS=gfx1030 (compiler target)
  • PATH includes ~/.bun/bin and ~/.cargo/bin

Build Time: ~8–12 minutes on GitHub Actions
Image Size: ~3.2 GB (compressed in registry)

vllm/Dockerfile Specification

Multi-Stage Build: Separates compilation (build) from minimal runtime (final)
Purpose: Compile vLLM source against ROCm 7.2.4 + PyTorch wheels

Build Stage – Dependency Matrix

Component	Version	Source
vLLM	v0.25.0	lm-sys/vllm GitHub (cloned at build time)
PyTorch	2.10.0+rocm7.2.4.lw	repo.radeon.com wheels
Triton	3.6.0+rocm7.2.4	repo.radeon.com wheels
Transformers	>=4.53.0	pip (PyPI)
Ray	[default]>=2.40.0	pip (distributed serving)
FastAPI	>=0.115.0	pip (OpenAI API)
NumPy	>=1.26,<2.1	pip
Python	3.12	venv isolated

Critical Build Environment Variables

# Target Architecture
export PYTORCH_ROCM_ARCH=gfx1030
export GPU_TARGETS=gfx1030
export HSA_OVERRIDE_GFX_VERSION=10.3.0

# vLLM Compilation Flags
export BUILD_FA=0                        # Disable custom Flash-Attention build
export VLLM_FLASH_ATTN=0                 # Fall back to PyTorch reference
export VLLM_FLASHINFER=0                 # Disable FlashInfer (NVIDIA only)
export VLLM_TARGET_DEVICE=rocm           # HIP compilation backend
export VLLM_USE_TRITON_FLASH_ATTN=0      # Disable Triton Flash-Attn

# Memory & Performance
export PYTORCH_ALLOC_CONF=expandable_segments:True  # Fragmentation mitigation
export MAX_JOBS=$(nproc)                           # Parallel build jobs

Build Process Flow

1. Create Python 3.12 virtual environment
           ↓
2. Install build tools (cmake, ninja, setuptools, setuptools-scm)
           ↓
3. Download ROCm wheels from repo.radeon.com
     ├─ torch-2.10.0+rocm7.2.4.lw.git3d3aa833-cp312-...-linux_x86_64.whl
     ├─ torchvision-0.25.0+rocm7.2.4.git82df5f59-cp312-...-linux_x86_64.whl
     ├─ torchaudio-2.10.0+rocm7.2.4.git5047768f-cp312-...-linux_x86_64.whl
     └─ triton-3.6.0+rocm7.2.4.git4ed88892-cp312-...-linux_x86_64.whl
           ↓
4. Install PyTorch + Triton into venv
           ↓
5. Run torch.cuda.is_available() sanity check
           ↓
6. Install vLLM runtime dependencies (transformers, fastapi, ray, etc.)
           ↓
7. Clone vLLM source: git clone --depth 1 --branch v0.25.x https://...
           ↓
8. Build vLLM C++ extensions:
     ├─ LLVM IR codegen for gfx1030
     ├─ rocBLAS kernel linking
     ├─ HIP runtime integration
     └─ vLLM custom CUDA→HIP kernel translation
           ↓
9. Install compiled vLLM into venv (pip install . --no-build-isolation)
           ↓
10. Verify import: python -c "import vllm; print(vllm.__version__)"
           ↓
11. Strip build tools (cmake, ninja removed to shrink final image)

Build Time: ~45–90 minutes on GitHub Actions (depends on ccache hits)
Build Log Artifact: Saved to /workspace/vllm_build.log for CI debugging

Runtime Stage – Final Image

Copy venv from build stage (/opt/venv)
        ↓
Remove PEP 668 restriction (allow pip in container)
        ↓
Create model cache directories:
  • /workspace/models          (model weights)
  • /workspace/.cache/huggingface (HF downloads)
        ↓
Expose port 8000 (OpenAI API)
        ↓
Default CMD: python -m vllm.entrypoints.openai.api_server

Final Image Size: ~2.8 GB (runtime-only, no build artifacts)

GitHub Actions CI/CD Pipeline

Trigger Conditions

Workflow	Triggers	Registry Path
`build-base.yml`	Push to `Dockerfile.base` OR manual workflow_dispatch	`ghcr.io/sar/v620/rocm-base`
`build-vllm.yml`	Push to `vllm/Dockerfile` OR manual workflow_dispatch	`ghcr.io/sar/v620/vllm-gfx1030`

Resource Management

Both workflows include aggressive disk cleanup before builds:

# Reclaim ~30 GB of runner disk space
• Remove Android SDK                    (~10 GB)
• Remove .NET runtime                   (~3 GB)
• Remove Haskell                        (~5 GB)
• Remove LLVM, Mono, PHP, Swift         (~15 GB)
• Remove pre-cached Docker images       (~3 GB)
• Remove /usr/local (hostedtoolcache)   (~2 GB)
• Disable and clear swap                (~4 GB)

Total Reclaimed: ~30–35 GB (GitHub Actions runner has 85 GB total disk)

Build & Push Steps

1. Checkout repository (actions/checkout@v4)
           ↓
2. Authenticate to GHCR (docker/login-action@v3)
           ├─ Registry: ghcr.io
           ├─ Username: ${{ github.actor }}
           └─ Token: ${{ secrets.GITHUB_TOKEN }} (automatic)
           ↓
3. Free disk space (~30 GB)
           ↓
4. Set up Docker Buildx (docker/setup-buildx-action@v3)
           ├─ Enables inline cache export to GitHub Actions cache
           └─ Uses BuildKit for optimized layer caching
           ↓
5. Extract image metadata (docker/metadata-action@v5)
           ├─ Git SHA tag (type=sha, format=long)
           └─ 'latest' tag (only on main branch)
           ↓
6. Build & push image (docker/build-push-action@v5 or v6)
           ├─ Context: ./ (repo root)
           ├─ File: ./Dockerfile.base or ./vllm/Dockerfile
           ├─ Build args: BASE_IMAGE=$REGISTRY/$BASE_IMAGE_NAME:latest (vllm only)
           ├─ Push: true
           └─ Cache: GitHub Actions cache
           ↓
7. Image pushed to GitHub Container Registry

Example: Trigger vLLM Build

# Push to vllm/Dockerfile triggers build-vllm.yml
git add vllm/Dockerfile
git commit -m "Update vLLM dependencies"
git push origin main

# Workflow automatically starts within seconds
# Image available at: ghcr.io/sar/v620/vllm-gfx1030:latest
#                   ghcr.io/sar/v620/vllm-gfx1030:<git-sha>

Supported LLM Models & Memory Requirements

Single-GPU Configurations (32 GB V620)

Model	Parameters	Quantization	Required VRAM	Notes
Qwen3.5-9B	9B	FP16	20–22 GB	Fits with room for 64K context
Qwen3.5-9B	9B	INT8	10–12 GB	vLLM PagedAttention overhead
Qwen3.5-9B	9B	INT4 (AWQ/GPTQ)	5–7 GB	Minimal quality loss, 128K context supported
Mistral-7B-Instruct	7B	FP16	15 GB	Legacy baseline
Mistral-7B-Instruct	7B	INT4	4 GB	–

Multi-GPU Configurations (Tensor Parallelism)

4× V620 Setup (128 GB Total VRAM)

Physical Topology:
┌─────────────┐
│  V620 #0    │ GPU 0: Layers 0–8 (FP16)
├─────────────┤
│  V620 #1    │ GPU 1: Layers 9–16 (FP16)
├─────────────┤
│  V620 #2    │ GPU 2: Layers 17–24 (FP16)
├─────────────┤
│  V620 #3    │ GPU 3: Layers 25–32 (FP16)
└─────────────┘
       ↑
   vLLM Tensor Parallel
   Request Router

Model	Parameters	Quantization	Configuration	Per-GPU VRAM	Total VRAM	Throughput
MiniMax-M2.7-AWQ-4bit	229B	INT4 (W4A16)	`--tensor-parallel-size 4`	~32 GB	128 GB	Fits w/ 2–4 GB headroom
MiniMax-M2.7	229B	FP8	`--tensor-parallel-size 4`	~57 GB	228 GB	OOM (exceeds 128 GB)

MiniMax M2.7 Deployment on v620 Cluster (4 GPUs):

docker run --rm -it \
  --network=host \
  -e HF_TOKEN=$HF_TOKEN \
  --device=/dev/kfd \
  --device=/dev/dri \
  --group-add=video \
  --ipc=host \
  --shm-size=32G \
  -v /models:/workspace/models \
  ghcr.io/sar/v620/vllm-gfx1030:latest \
  python -m vllm.entrypoints.openai.api_server \
    --model cyankiwi/MiniMax-M2.7-AWQ-4bit \
    --trust-remote-code \
    --tensor-parallel-size 4 \
    --distributed-executor-backend ray \
    --load-format fastsafetensors \
    --enable-auto-tool-choice \
    --tool-call-parser minimax_m2 \

vLLM Inference Runtime

vLLM (Virtual Large Language Model) is a high-throughput LLM serving engine optimized for production deployments. The v620 repository enables compilation directly from vLLM source for RDNA2 targets.

Key Architecture Elements for RDNA2

PagedAttention Memory Management
vLLM uses a paged KV cache abstraction that partitions attention key/value states into fixed-size blocks (typically 16 tokens per page). This approach eliminates contiguous memory allocation requirements for attention states, enabling:

40-50% reduction in KV cache memory footprint
Support for very long context windows (128K+ tokens)
Efficient batch scheduling across variable-length sequences

Continuous Batching Dispatch
Instead of static batching (wait for fixed-size batch), vLLM interleaves token generation from multiple sequences:

Prefill phase (first-token latency) processes prompt tokens
Decode phase (token generation) auto-schedules new requests as slots free
RDNA2's compute-to-memory ratio (20.28 TFLOPS @ 512 GB/s = 0.04 FLOPS/byte) benefits from high occupancy across diverse sequence lengths

Flash-Attention Integration
vLLM pairs with optimized Flash-Attention implementations:

Triton Flash-Attention (AMD's optimization): Block-wise computation with tiling to maximize L1/LDS cache hit rates
CK Flash-Attention (Composable Kernel library): Hand-tuned for CDNA but with RDNA2 fallback support
Reduces attention complexity from O(N²) memory access to O(N) via selective attention computation

Quantization Support for Reduced Footprint

RDNA2 models typically require quantization to fit within 32 GB VRAM constraints:

Quantization	Bits/Weight	7B Model Size	13B Model Size	70B Model Size
FP16 (baseline)	16	~14 GB	~26 GB	~140 GB (OOM)
8-bit	8	~7 GB	~13 GB	~70 GB
4-bit (GPTQ/AWQ)	4	~3.5 GB	~6.5 GB	~35 GB
2-bit	2	~1.75 GB	~3.25 GB	~17.5 GB

vLLM supports GPTQ (post-training quantization), AWQ (activation-aware quantization), and GGUF format models for RDNA2 inference.

Build & Deployment Architecture

Container Layering Strategy

rocm/pytorch:{rocm-version} (base image with HIP driver, PyTorch)
    ↓
Dockerfile.base (ROCm SDK, compilation toolchain, system dependencies)
    ↓
vllm/ (vLLM source compilation, kernel optimization, testing)
    ↓
Production inference image (optimized runtime)

Compilation Pipeline for RDNA2/GFX1030

The build process targets the gfx1030 LLVM architecture through environment configuration:

export PYTORCH_ROCM_ARCH="gfx1030"
export AMDGPU_TARGETS="gfx1030"

This ensures:

LLVM IR → GCN ISA translation for RDNA2 instruction set (Wave64 native)
AMD GPU Compiler (AMDGCN) backend selects dual-issue VALU instructions, MFMA/DP4A operations
Kernel library linking pulls optimized implementations from rocBLAS (linear algebra), rocWMMA (matrix operations)

Critical Environment Variables

Variable	Purpose	RDNA2 Value
`PYTORCH_ROCM_ARCH`	Target GPU architecture	`gfx1030`
`AMDGPU_TARGETS`	AMD compiler target	`gfx1030`
`ROCM_HOME`	ROCm installation root	`/opt/rocm`
`LD_LIBRARY_PATH`	Dynamic linker search paths	`$ROCM_HOME/lib:...`
`VLLM_USE_TRITON_FLASH_ATTN`	Flash-Attention backend selector	`1` (Triton, default) or `0` (CK/PyTorch)
`FLASH_ATTENTION_TRITON_AMD_ENABLE`	Enable Triton FA for AMD	`TRUE`

Deployment Workflow

1. Build Base Image (One-time)

docker build -f Dockerfile.base -t v620-rocm:base .

Produces foundation layer with:

ROCm 6.2+ runtime + LLVM toolchain
PyTorch compiled for gfx1030
CMake, CUDA-like build tools

2. Compile vLLM Source

docker build -f vllm/Dockerfile -t v620-vllm:latest .

Triggers:

Clone upstream vLLM from GitHub
Configure PYTORCH_ROCM_ARCH=gfx1030
Compile Flash-Attention for RDNA2 (either Triton or CK variant)
Build vLLM C++ extensions (custom CUDA kernels → HIP)
Unit tests against reference models

3. Runtime Execution

docker run -it \
  --network=host \
  --device=/dev/kfd --device=/dev/dri \
  --group-add=video \
  --ipc=host \
  --shm-size=16G \
  -v $(pwd)/models:/data \
  v620-vllm:latest \
  python -m vllm.entrypoints.openai.api_server \
    --model /data/Meta-Llama-3-8B-Instruct-GPTQ \
    --quantization gptq \
    --port 8000

Opens OpenAI-compatible REST API at http://localhost:8000/v1/completions.

Kernel Tuning & Optimization Pathways

Memory Hierarchy on RDNA2

L1 Cache (16 KB / CU, 64 KB per WaveGroup)
    ↓
L2 Cache (128 MB shared, 512 GB/s bandwidth)
    ↓
Main VRAM (32 GB GDDR6)

Optimization Focus:

Attention kernels: Maximize L2 hit rate by partitioning KV cache blocks
Linear layers (MatMul): Use rocBLAS tuning for GEMM operation sizing
Position embeddings: Pre-compute rotary/alibi embeddings in shared memory (LDS)

Performance Tuning Flags

Tuning	Impact	Configuration
Wave occupancy	Instruction-level parallelism	Target 50-80% of max waves (400+ for V620)
LDS bank conflicts	Memory throughput within CU	Transpose data layouts to avoid stride-32 patterns
VRAM coalescing	Global memory bandwidth	Align loads to 128-byte boundaries
FMA scheduling	Instruction issue rate	Interleave VALU + MFMA chains

Integration with ROCm Ecosystem

Virtualization (SR-IOV) for Multi-Tenant Serving

The Radeon PRO V620 supports GPU partitioning via Single-Root I/O Virtualization (SR-IOV), enabling multiple inference VMs to share a single GPU:

# Enable in BIOS: IOMMU + SR-IOV
modprobe gim vf_num=4  # Partition into 4 virtual GPUs

# Each VM sees isolated GPU: /dev/dri/renderD128, etc.

vLLM benefit: Run separate isolated inference servers per tenant with QoS isolation at firmware level.

Multi-GPU Inference (Tensor Parallelism)

For models exceeding 32 GB (e.g., 70B), vLLM can distribute layers across multiple V620s:

python -m vllm.entrypoints.openai.api_server \
  --model Meta-Llama-3-70B \
  --tensor-parallel-size 2 \  # Split across 2 GPUs
  --pipeline-parallel-size 1

Per-GPU communication: Uses PCIe Gen4 x16 (32 GB/s bidirectional). Profiling tool: rocm-smi --json-dumps profiling.

Known Limitations & Workarounds

Issue	Root Cause	Mitigation
Flash-Attention compile failures	Triton AMD codegen incomplete for gfx1030	Set `VLLM_USE_TRITON_FLASH_ATTN=0` to fall back to PyTorch naive attention (slower)
amdsmi crashes	ROCm Python binding conflicts	Uninstall `amdsmi`, reinstall from `/opt/rocm/share/amd_smi` after build
Long context (>32K tokens)	KV cache OOM even with PagedAttention	Use quantized KV cache (8-bit), or reduce batch size via `--max-num-seqs`
First-token latency > 500ms	Prefill phase bottleneck on RDNA2	Enable speculative decoding or async prefix caching with `--enable-prefix-caching`

Fine-Tuning on RDNA2

The repository supports fine-tuning workflows via PyTorch + bitsandbytes quantization:

from transformers import AutoModelForCausalLM
from peft import get_peft_model, LoraConfig

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b",
    device_map="auto",
    load_in_8bit=True  # rocBLAS INT8 support on RDNA2
)

# LoRA adapter for memory efficiency
lora_config = LoraConfig(r=16, lora_alpha=32)
model = get_peft_model(model, lora_config)

Throughput: ~35 samples/second per V620 on 7B model (4-GPU batch) with gradient checkpointing.

Monitoring & Profiling

Real-time GPU Metrics

rocm-smi --json-dumps profiling  # Memory, temperature, power, utilization
watch -n1 rocm-smi --showuse

vLLM Request Tracing

Enable structured logging:

import logging
logging.basicConfig(level=logging.DEBUG)

# Access latency breakdown: e_2e, prefill_latency, decode_latency
response = requests.post("http://localhost:8000/v1/completions", json={
    "model": "model-name",
    "prompt": "...",
})

Profiling Kernels (rocprof)

rocprof --stats python -m vllm.entrypoints.openai.api_server --model llama-7b
# Generates .csv with kernel execution counts, duration, FLOPs

Validation & Testing

The .github/workflows/ pipeline includes:

Compilation correctness: Ensure no linking errors for gfx1030
Functional tests: Small models (Tiny Llama) with known output
Performance regression: Compare decode throughput vs baseline
Container layer size: Warn if runtime image exceeds 8GB

Conclusion

v620 streamlines the path from vLLM source to production inference on AMD's professional RDNA2 GPU. By automating environment setup, kernel tuning, and containerization, it reduces operational friction for inference engineers deploying LLM endpoints on Radeon PRO V620 datacenter hardware.

For multi-tenant inference requiring GPU virtualization, consumer RDNA hardware, or alternative inference engines, refer to Llama.cpp (ROCm) or HIPFire as complementary tools within the AMD inference ecosystem.

AMD ROCm Documentation: https://rocm.docs.amd.com
vLLM Project: https://github.com/lm-sys/vllm
Radeon PRO V620 Specifications: https://www.amd.com/en/products/accelerators/radeon-pro/amd-radeon-pro-v620.htmlinference engineers deploying LLM endpoints on Radeon PRO V620 datacenter hardware.

For multi-tenant inference requiring GPU virtualization, consumer RDNA hardware, or alternative inference engines, refer to Llama.cpp (ROCm) or HIPFire as complementary tools within the AMD inference ecosystem.

AMD ROCm Documentation: https://rocm.docs.amd.com vLLM Project: https://github.com/lm-sys/vllm Radeon PRO V620 Specifications: https://www.amd.com/en/products/accelerators/radeon-pro/amd-radeon-pro-v620.html

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
.github/workflows		.github/workflows
.static		.static
vllm		vllm
Dockerfile.base		Dockerfile.base
README.md		README.md

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