This repository provides Fedora Rawhide-based containers for working with Ryzen AI MAX+ 395 Strix Halo chips with integrated GPU (gfx1151) and unified memory. The containers come pre-built with llama.cpp and all necessary GPU compute libraries.

TL;DR - Performance Summary

After extensive testing, Vulkan is currently the most stable and performant option for Strix Halo GPUs:

Backend	Status	Notes
Vulkan	✅ Recommended	Most stable, best performance across all model sizes
ROCm 6.4.2	⚠️ Limited	Works ok, but extremely slow past 64GB memory allocations
ROCm 7.0 beta	❌ Unstable	Frequent crashes under heavy load (llama-bench), basic usage possible

Available Containers

Container	Backend	Status	Use Case
`vulkan`	Vulkan compute	Stable	Primary recommendation
`rocm-6.4.2`	ROCm 6.4.2 (HIP)	Stable for <64GB models	Smaller models only
`rocm-7beta`	ROCm 7.0 beta (HIP)	Beta/Unstable	Testing only

All containers include up-to-date libraries from Fedora Rawhide, except ROCm 7.0 beta which uses official AMD RPMs.

Prerequisites

Podman (or Docker with alias)
Toolbox
Linux kernel with AMD GPU (amdgpu) drivers
AMD Strix Halo GPU with proper host configuration (see below)

Quick Start

1. Pull Pre-built Images

# Recommended: Vulkan (most stable)
podman pull docker.io/kyuz0/amd-strix-halo-toolboxes:vulkan

# Optional: ROCm variants for testing
podman pull docker.io/kyuz0/amd-strix-halo-toolboxes:rocm-6.4.2
podman pull docker.io/kyuz0/amd-strix-halo-toolboxes:rocm-7beta

2. Create Toolboxes

For Vulkan (Recommended):

toolbox create llama-vulkan \
  --image docker.io/kyuz0/amd-strix-halo-toolboxes:vulkan \
  -- \
    --device /dev/dri \
    --group-add video \
    --security-opt seccomp=unconfined

For ROCm 6.4.2:

toolbox create llama-rocm-6.4.2 \
  --image docker.io/kyuz0/amd-strix-halo-toolboxes:rocm-6.4.2 \
  -- \
    --device /dev/kfd \
    --device /dev/dri \
    --group-add video \
    --security-opt seccomp=unconfined

For ROCm 7.0 beta:

toolbox create llama-rocm-7beta \
  --image docker.io/kyuz0/amd-strix-halo-toolboxes:rocm-7beta \
  -- \
    --device /dev/kfd \
    --device /dev/dri \
    --group-add video \
    --security-opt seccomp=unconfined

Note: The -- separator passes the remaining flags to Podman/Docker for GPU access.

3. Enter and Test

Test Vulkan container:

toolbox enter llama-vulkan
vulkaninfo | head -n 10
llama-cli --list-devices

Test ROCm containers:

toolbox enter llama-rocm-6.4.2
llama-cli --list-devices
rocm-smi

Performance Benchmarks

All benchmarks performed on HP Z2 Mini G1a with 128GB RAM, using llama-bench with all layers offloaded to GPU.

Prompt Processing (pp512) - tokens/second

Model	Size	Params	Vulkan	ROCm 6.4.2	ROCm 7 Beta	Winner
Gemma3 12B Q8_0	13.40 GiB	11.77B	509.45 ± 1.01	224.43 ± 0.26	219.55 ± 0.41	🏆 Vulkan (+132%)
Qwen3 MoE 30B.A3B BF16	56.89 GiB	30.53B	74.62 ± 0.63	157.87 ± 2.71	155.37 ± 2.64	🏆 ROCm 6.4.2 (+112%)
Llama4 17Bx16E (Scout) Q4_K	57.73 GiB	107.77B	136.47 ± 1.52	132.61 ± 0.65	❌ GPU Hang	🏆 Vulkan (+3%)
Qwen3 MoE 235B.A22B Q3_K	96.99 GiB	235.09B	59.12 ± 0.39	⚠️ Too slow	⚠️ Too slow	🏆 Vulkan only

Text Generation (tg128) - tokens/second

Model	Size	Params	Vulkan	ROCm 6.4.2	ROCm 7 Beta	Winner
Gemma3 12B Q8_0	13.40 GiB	11.77B	13.67 ± 0.01	13.80 ± 0.00	13.43 ± 0.00	🏆 ROCm 6.4.2 (+1%)
Qwen3 MoE 30B.A3B BF16	56.89 GiB	30.53B	7.36 ± 0.00	23.67 ± 0.02	22.21 ± 0.00	🏆 ROCm 6.4.2 (+222%)
Llama4 17Bx16E (Scout) Q4_K	57.73 GiB	107.77B	20.05 ± 0.00	17.61 ± 0.00	❌ GPU Hang	🏆 Vulkan (+14%)
Qwen3 MoE 235B.A22B Q3_K	96.99 GiB	235.09B	15.97 ± 0.02	⚠️ Too slow	⚠️ Too slow	🏆 Vulkan only

Performance Summary

🏆 Vulkan Advantages:

Consistently stable across all model sizes
Significantly better prompt processing on smaller quantized models (127% faster on Gemma3 12B)
Only option that can handle >64GB models efficiently
Moderate advantage on larger quantized models (3-14% better on Llama4 17B)

🏆 ROCm 6.4.2 Advantages:

Dramatically superior performance on BF16 models (112% faster prompt processing, 222% faster text generation on Qwen3 MoE 30B)
Optimized native floating-point operations through HIP compute
Better suited for models using native precision formats

📊 Performance by Model Type:

BF16/Native Precision Models: ROCm 6.4.2 is the clear winner with 2-3x better performance
Small Quantized Models: Vulkan has significant advantages for prompt processing
Large Quantized Models: Performance is similar between backends (differences within noise)
Large Models (>64GB): Vulkan is the only viable option due to ROCm's memory allocation issues

❌ ROCm 6.4.2 Limitations:

Extremely slow memory loading for models >64GB (unusable)
Performance advantage limited to BF16/native precision models

❌ ROCm 7.0 Beta Issues:

GPU hangs/crashes on larger models (Llama4 17B causes "GPU Hang" and core dump)
Similar slow loading issues as ROCm 6.4.2 for models >64GB
Performance similar to ROCm 6.4.2 when it works, but reliability is poor
Uses official AMD RPMs (beta quality)

💡 Recommendation Strategy:

Use ROCm 6.4.2 for BF16/native precision models under 64GB
Use Vulkan for quantized models (especially smaller ones) and all models over 64GB
For large quantized models under 64GB, either backend performs similarly
Avoid ROCm 7.0 beta for production workloads

Building Containers Locally (Optional)

If you prefer to build the containers yourself:

# Build all variants
podman build -t localhost/llama-vulkan -f Dockerfile.vulkan .
podman build -t localhost/llama-rocm-6.4.2 -f Dockerfile.rocm-6.4.2 .
podman build -t localhost/llama-rocm-7beta -f Dockerfile.rocm-7beta .

Create Toolboxes from Local Images

# Using locally built images
toolbox create llama-vulkan-local \
  --image localhost/llama-vulkan \
  -- \
    --device /dev/dri \
    --group-add video \
    --security-opt seccomp=unconfined

toolbox create llama-rocm-local \
  --image localhost/llama-rocm-6.4.2 \
  -- \
    --device /dev/kfd \
    --device /dev/dri \
    --group-add video \
    --security-opt seccomp=unconfined

Host Configuration

This should work on any Strix Halo device. For a complete list of available hardware, see: Strix Halo Hardware Database

My Test Configuration

Component	Specification
Test Machine	HP Z2 Mini G1a
CPU	Ryzen AI MAX+ 395 "Strix Halo"
System Memory	128 GB RAM
GPU Memory	512 MB allocated in BIOS
Host OS	Fedora 42, kernel 6.15.6-200.fc42.x86_64

Kernel Parameters

Add these boot parameters to enable unified memory and optimal performance:

amd_iommu=off amdgpu.gttsize=131072 ttm.pages_limit=335544321

Parameter	Purpose
`amd_iommu=off`	Disables IOMMU for lower latency
`amdgpu.gttsize=131072`	Enables unified GPU/system memory (up to 128 GB)
`ttm.pages_limit=335544321`	Allows large pinned memory allocations

Apply the changes:

# Edit /etc/default/grub to add parameters to GRUB_CMDLINE_LINUX
sudo grub2-mkconfig -o /boot/grub2/grub.cfg
sudo reboot

Troubleshooting

Common Issues

Issue	Solution
GPU not detected	Verify `/dev/dri` and `/dev/kfd` devices exist on host
Memory errors	Check that kernel parameters are properly applied
Permission denied	Ensure your user is in the `video` group
ROCm crashes	Try Vulkan backend instead
Slow loading (>64GB models)	Use Vulkan instead of ROCm for large models

Verify GPU Access

# Check devices
ls -la /dev/dri /dev/kfd

# Check ROCm (in ROCm containers)
rocm-smi

# Check Vulkan (in Vulkan container)
vulkaninfo --summary