Deploying Distributed vLLM Inference on an AMD Strix Halo RDMA Cluster

A detailed technical implementation guide shows how to cluster two AMD Strix Halo APUs via an Intel E810 NIC using RoCE v2 for tensor-parallelized vLLM execution. By bypassing the CPU kernel with RDMA and tuning the kernel's Graphics Translation Table, the system reduces inter-node synchronization latency to 5 microseconds.

Interconnect Architecture and Hardware Constraints

Deploying distributed LLM inference across consumer-grade APU hardware requires resolving substantial physical and logical bottlenecks. This architecture couples two Framework Desktop Mainboards powered by the AMD Ryzen AI MAX+ "Strix Halo" APU, each configured with 128GB of Unified Memory.

Because the Framework motherboard's PCIe slot is physically limited to x4 wiring, mounting the dual-port 100GbE Intel E810-CQDA1 network interface card requires a physical PCIe x4-to-x16 riser card. Testing indicates this physical constraint does not bottleneck the direct-attach copper link; the configuration delivers approximately 50Gbps of bandwidth and maintains a 5µs point-to-point latency without the deployment of an intermediary network switch.

Kernel and BIOS Tuning for Unified Memory

To enable high-performance memory sharing between the host system and the integrated GPU, physical BIOS and kernel-level configurations must be explicitly defined. The BIOS integrated GPU memory allocation must be set to its minimum value of 512MB. This forces the system to rely on the Graphics Translation Table to dynamically allocate system RAM as Unified Memory for ROCm tasks.

To prevent physical address translation overhead and resource contention, the Fedora 43 host systems (running tested kernels 6.18.5 or 6.18.6) must be configured with specific boot parameters. The following arguments are appended to the GRUB command line:

• `iommu=pt`: Configures the IOMMU to pass-through mode, eliminating translation overhead for both the RDMA NIC and the dynamic iGPU memory allocations.
• `pci=realloc`: Forces PCIe Base Address Register reallocation, ensuring the system can map the massive address space required for unified system memory and local registers.
• `pcie_aspm=off`: Disables Active State Power Management to prevent link state transitions from introducing latency spikes.
• `amdgpu.gttsize=126976`: Defines the Graphics Translation Table boundary size.
• `ttm.pages_limit=32505856`: Sets the page limits for the Translation Table Manager.

Network Stack and RDMA Configuration

The cluster operates on a direct `/30` private subnet (192.168.100.0/30) utilizing Jumbo Frames. The network interfaces are configured for MTU 9000 to minimize frame segmentation overhead. The physical link is driven by the native, in-kernel `ice` Ethernet driver, while the RoCE v2 capabilities are managed by the unified `irdma` driver.

Userspace RDMA configuration relies on standard Fedora packages, specifically `rdma-core`, `libibverbs-utils`, and `perftest`. Correct hardware initialization is verified by confirming the Intel E810 non-volatile memory firmware is at version 4.91 (or newer) using `ethtool` diagnostics. Once active, the RDMA link state transitions to `ACTIVE`, enabling low-level InfiniBand verbs execution between the two nodes.

Ray and RCCL Orchestration for Tensor Parallelism

Distributed execution across the APUs is orchestrated at two layers: Ray manages the control plane, while the ROCm Collective Communication Library (RCCL) handles the high-performance data plane. In a Tensor Parallelism (TP=2) configuration, the nodes must synchronize partial mathematical tensors after every single neural network layer, demanding thousands of operations per second.

Standard TCP/IP network layers introduce 70µs to 100µs of system latency, rendering real-time token generation unusable. Implementing RoCE v2 bypasses both the host operating system kernel and CPU, writing data directly between the physical memory spaces of each node. This drops execution latency to approximately 5µs.

To align the software stack with the RDMA fabric, the system runs inside a containerized environment deployed via a specialized helper script (`refresh_toolbox.sh`). This script detects the host's InfiniBand device paths, mounts the devices inside the container, and patches the system runtime with a custom compiled `librccl.so` shared library. Once deployed, the operator uses a terminal user interface (`start-vllm-cluster`) to initialize the distributed Ray runtime and launch vLLM serving.