Building an NVLink-Enabled Inference Cluster with RISC-V Hosts

Stop waiting weeks for a reproducible GPU inference lab — build an NVLink Fusion cluster with RISC‑V hosts in days

If you’re a platform engineer, ML infra lead, or dev working on production inference, you’ve felt the pain: brittle, costly GPU stacks, unpredictable interconnect performance, and teams wasting cycles on environment drift. In 2026 the landscape is changing — SiFive announced NVLink Fusion integration with RISC‑V IP (late 2025), and GPU interconnects now support fabrics that make low‑latency, pooled inference feasible across heterogeneous hosts. This guide walks you through designing and deploying an efficient, reproducible inference cluster that pairs SiFive RISC‑V SoCs with Nvidia GPUs over NVLink Fusion, and integrates cleanly with Kubernetes using device plugins and topology-aware scheduling.

Executive summary: what you’ll get and why it matters (top-level)

• Hardware checklist for RISC‑V hosts and NVLink‑capable GPUs and switches.
• Three practical topology patterns (local pooled GPUs, fabric‑shared GPUs, and disaggregated inference nodes) and when to use each.
• Step‑by‑step Kubernetes integration: drivers, NVIDIA GPU Operator, custom NVLink Fusion device plugin, Node Feature Discovery, and Topology Manager strategies.
• Performance tuning: NUMA, CPU affinity, hugepages, UCX/GDR, and RDMA tips for low‑latency inference.
• Reproducible deployment patterns (containers, Jupyter, CI/CD) and security guidance for multi‑tenant labs.

Context and 2026 trends you must consider

By 2026, heterogenous compute has moved from experimental to mainstream for inference. Two trends matter for this guide:

1. RISC‑V gains traction in AI appliances: SiFive’s NVLink Fusion integration (announced in late 2025) enables RISC‑V silicon to participate directly in GPU fabric, lowering latency and enabling tighter CPU/GPU coordination at the host level.
2. Fabric‑level GPU sharing: NVLink Fusion and fabric switches make it possible to present GPUs as pooled resources across multiple host CPUs while preserving low‑latency, cache‑coherent access patterns.

Design choices: hardware selection and topology

Start with clear goals: latency bound (p99 target), model size, and expected concurrency. These drive both hardware and topology choices.

Hardware checklist (minimum viable stack)

• SiFive RISC‑V SoCs or vendor boards with NVLink Fusion IP enabled and upstream BSPs for Linux. Confirm vendor support for GPU drivers and NVLink firmware components.
• Nvidia DGX‑class or data center GPUs that support NVLink Fusion / fabric (verify the GPU model and firmware compatibility with NVLink Fusion fabric switches). In 2026, modern data‑center GPUs continue to include NVLink fabric support — validate with vendor datasheets.
• NVLink Fusion switches (fabric switches or NVLink bridges) for multi‑GPU aggregation and inter‑host fabric connectivity.
• PCIe/NICs and RDMA‑capable networking for fallback and management; 100GbE+/400GbE recommended for host control and storage traffic.
• Storage: fast shared object store (S3/GCS) for model artifacts plus local NVMe for model cache and swap.
• Management host running Kubernetes control plane (can be x86) and provisioning tools (PXE/iPXE if you need automated bare‑metal installs).

Topology patterns (choose based on your workload)

1. Local‑attached GPU nodes — each RISC‑V host has one or more GPUs directly attached via NVLink. Best for low‑latency single‑node inference and simpler device management. Use when p99 latency < 2ms is required and working set fits per‑node GPU memory.
2. Fabric‑pooled GPUs — GPUs are connected to an NVLink Fusion fabric switch and exposed to RISC‑V hosts across the fabric. Use this when models exceed per‑GPU memory or you want dynamic GPU sharing across hosts. Requires topology‑aware scheduling and a device plugin that can represent NVLink fabric mappings.
3. Disaggregated inference nodes — separate control-plane RISC‑V nodes orchestrating stateless inference containers that access GPUs over NVLink/UCX. Best when you need strict isolation and multi‑tenant quotas.

Software stack — OS to containers

On each RISC‑V host, you’ll run a Linux kernel with vendor‑supplied BSPs. The key software components you need:

• Upstream or vendor kernel with NVLink Fusion and IOMMU support enabled.
• NVIDIA drivers and low‑level NVLink firmware package supplied by the GPU vendor (confirm RISC‑V host compatibility).
• NVIDIA Container Toolkit (or vendor equivalent) to expose GPUs into containers.
• containerd or CRI‑compatible runtime for Kubernetes.
• NVIDIA GPU Operator for driver lifecycle where supported, extended with an NVLink Fusion device plugin to advertise fabric topology.
• Node Feature Discovery (NFD) to expose host attributes (NVLink topology labels) to Kubernetes.
• Kubernetes Topology Manager and extended device plugin API (or custom device plugin) for topology‑aware allocation.

Step‑by‑step: Getting the first node online

1. Provision the RISC‑V host OS

   Install a vendor Linux image with the kernel and vendor firmware. Ensure IOMMU, NUMA, and any RISC‑V specific device nodes for NVLink are enabled. Work with your SiFive board BSP to get kernel modules and firmware in place.

2. Install GPU drivers and verify NVLink

   Install the vendor NVIDIA driver package that bundles NVLink Fusion support. Verify NVLink fabric visibility with vendor tools (fabric status, peer access). Expected checks:

   # verify GPUs
   nvidia-smi topo -m
   # or vendor-specific fabric status
   vendor-nvlink-fusion status
         

3. Container runtime

   Install containerd and the NVIDIA Container Toolkit (or vendor toolkit) so containers can access GPUs. Configure runtime class for GPU workloads.

4. Deploy Kubernetes (control-plane can be x86)

   Use kubeadm or a distribution (K3s, MicroK8s, OpenShift) that supports device plugins and custom scheduling. Ensure the Topology Manager policy is set to best-effort or single-numa-node depending on your latency needs.

Kubernetes integration: device plugin and topology

The stock NVIDIA device plugin exposes GPU counts but not complex NVLink Fusion fabric topology. For fabric‑aware scheduling you’ll need to extend the stack.

1) Node Feature Discovery (NFD)

Use NFD to label nodes with NVLink fabric attributes (for example: fabric_id, connected_gpu_ids, zone). Sample NFD config snippet:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: nfd
spec:
  template:
    spec:
      containers:
      - name: nfd-worker
        image: myrepo/nfd:stable
        volumeMounts:
        - name: hwdata
          mountPath: /var/lib/nfd
      volumes:
      - name: hwdata
        hostPath:
          path: /etc/nfd

2) NVLink Fusion device plugin (custom)

Create a device plugin that:

• Discovers GPUs and their fabric links.
• Exposes resources like nvidia.com/gpu-fabric- or topological groups (e.g., nvlink_zone=0).
• Implements health checking and device allocation hooks compatible with the Device Plugin API.

High‑level device plugin flow (pseudo):

1. Probe /sys or vendor API to enumerate GPUs and NVLink peers
2. Build topology groups (local, peer, fabric_id)
3. Register resources with kubelet via Device Plugin API
4. On Allocate(): mount device nodes, set env vars describing fabric topology

3) Scheduling rules and Topology Manager

Use node labels from NFD and resource names from the device plugin in Pod specs. Example Pod spec with node selector and resource request:

apiVersion: v1
kind: Pod
metadata:
  name: inference-pod
spec:
  nodeSelector:
    nfd.io/nvlink-zone: "0"
  containers:
  - name: worker
    image: myrepo/inference:latest
    resources:
      limits:
        nvidia.com/gpu-fabric-0: 1

Enable the Kubernetes Topology Manager with the appropriate policy in kubelet config to keep CPU and fabric allocations aligned. For tight latency, use single-numa-node where possible and pin CPUs to the pod.

Performance tuning for low-latency inference

Low latency requires system‑level tuning. Use these practical steps:

• CPU pinning and isolcpus: isolate CPUs that run inference tasks. Use taskset or Kubernetes cpuManagerPolicy: static to pin vCPUs.
• NUMA alignment: ensure pods using GPU fabric grouped into the same NUMA domain exposed by NFD/device plugin mapping.
• Hugepages: enable hugepages for model allocator libraries (TensorRT, ONNX Runtime) to reduce TLB misses.
• Use UCX/GDR and GPUDirect: enable UCX for inter‑GPU and host‑GPU transfers to bypass kernel copies where supported by your GPU drivers and RISC‑V BSP.
• RDMA for control plane traffic: offload host network IO to RDMA NICs to avoid congesting GPU fabric paths.

Developer ergonomics: containers, Jupyter, and reproducible labs

Make it easy for data scientists and engineers to run inference experiments:

• Container images: build images that include the exact runtime (CUDA/cuDNN/ONNX/TensorRT) and declare device expectations via entrypoint scripts that read NVLink fabric environment variables exposed by the device plugin.
• Jupyter on GPU: run a JupyterHub deployment with GPU resourceClass images. Example Helm values (conceptual):

# jupyterhub values excerpt: concept-only
singleuser:
  image:
    name: myrepo/jupyter-gpu
    tag: 2026-optimized
  extraResources:
    limits:
      nvidia.com/gpu-fabric-0: 1

Use GitOps (ArgoCD) to manage these images and Helm charts so lab environments are reproducible.

Security, multi-tenancy and governance

When exposing fabric‑level resources you must plan for isolation and governance:

• RBAC and Namespace Quotas: use Kubernetes RBAC to control who can request fabric resources. Apply ResourceQuota to cap GPU fabric allocations per namespace.
• Network segmentation: separate management/control traffic from data and GPU fabric traffic with VLANs and RDMA zoning.
• Audit and telemetry: collect telemetry from device plugin and kernel NVLink telemetry to monitor health and topology changes.

CI/CD and reproducible experiments

Integrate inference tests in CI using ephemeral namespaces and GPU quotas. Pattern:

1. Build and push image with exact runtime.
2. Create ephemeral namespace and apply ResourceQuota including fabric resources.
3. Run validation with synthetic traces to verify p95/p99 latency.
4. Promote image if tests pass.

Debugging checklist: common issues and fixes

• GPU not visible in container — ensure Container Toolkit is installed and device plugin mounts /dev entries into the container.
• Pod scheduled on wrong node — check NFD labels and device plugin advertised resources; reconcile with node lease and kubelet logs.
• High latency between host and GPU — check NUMA alignment and NVLink peer access via vendor tooling; confirm UCX/GDR is active.
• Fabric topology changed after reboot — persist vendor firmware and confirm switch firmware versions across the fabric.

Case study (hypothetical, realistic)

Platform team at a mid‑sized AI company piloted a fabric‑pooled topology: 8 SiFive RISC‑V control hosts and 16 GPUs attached to two NVLink Fusion switches. They used an NVLink device plugin that exposed nvidia.com/gpu-fabric- resources plus NFD labels. After enabling Topology Manager with single-numa-node and applying CPU pinning, p99 latency for their 2‑shard Transformer inference dropped from 6.8ms to 2.1ms, while utilization improved due to fabric pooling.

Advanced strategies and future directions (2026+)

• Dynamic GPU aggregation: use scheduler extensions to aggregate GPU fabric slices per job — useful for bursty multi‑tenant inference.
• Memory disaggregation across NVLink: as NVLink fabrics mature, expect unified memory and cross‑host page migration to appear in vendor stacks; architect for these features now by exposing fabric capability labels.
• RISC‑V accelerators: offload pre/post processing to RISC‑V SoCs to reduce GPU occupancy and end‑to‑end latency.

“Treat NVLink Fusion as more than a faster interconnect — it’s a new resource class your scheduler must understand.”

Quickstart checklist — from zero to inference

1. Order hardware: SiFive RISC‑V boards with NVLink Fusion support, NVLink switches, and NV‑capable GPUs.
2. Provision OS with vendor BSP and confirm NVLink visibility.
3. Install container runtime + NVIDIA Container Toolkit; verify GPU in container.
4. Deploy Kubernetes with Topology Manager enabled and NFD DaemonSet.
5. Deploy GPU Operator and a custom NVLink Fusion device plugin as a DaemonSet.
6. Label nodes and run a small inference workload; iterate on CPU pinning and hugepages.

Actionable code snippets

Example of a minimal NVLink-aware device resource request in a Pod:

apiVersion: v1
kind: Pod
metadata:
  name: nvlink-infer
spec:
  nodeSelector:
    nfd.io/nvlink-zone: "0"
  containers:
  - name: server
    image: myrepo/inference:gpu-fusion
    resources:
      limits:
        nvidia.com/gpu-fabric-0: 1
    env:
      - name: NVLINK_ZONE
        valueFrom:
          fieldRef:
            fieldPath: metadata.labels['nfd.io/nvlink-zone']

Wrapping up — key takeaways

• NVLink Fusion plus RISC‑V changes the economics of inference by enabling low‑latency fabric sharing across heterogeneous hosts.
• Topology awareness is essential: use Node Feature Discovery, a custom device plugin, and Kubernetes Topology Manager to get consistent performance.
• Tune at the system level: NUMA, CPU pinning, hugepages, and UCX/GPUDirect deliver the last‑mile reductions in p99 latency.
• Automate and GitOps your lab: reproducible container images, JupyterHub templates, and CI validation for device resources minimize drift.

Next steps — deploy a reference architecture (call to action)

Ready to prototype? Download our reference repo (driver installation scripts, a sample NVLink Fusion device plugin, NFD templates, and Kubernetes manifests) and spin up an ephemeral test cluster. If you need help validating BSPs or customizing the device plugin for your vendor firmware, contact our engineering team for a hands‑on lab engagement.

Start a trial, download the reference codebase, or request a technical workshop: visit smart-labs.cloud/nvlink-riscv to get the repo and schedule a walkthrough.

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