AIclusterops

Building Heterogeneous AI Clusters: Best Practices for RISC-V Hosts Talking to Nvidia GPUs

nnet work

2026-02-07

11 min read

Operational guide for running RISC‑V hosts with NVLink‑attached Nvidia GPUs: kernel, drivers, orchestration & network best practices (2026).

Cut cluster costs and complexity: running RISC‑V hosts with Nvidia GPUs connected via NVLink Fusion

If you manage infrastructure for AI workloads, you already know the pain: vendor lock‑in, late driver support, brittle kernel modules and orchestration blind spots when new CPU architectures hit the datacenter. In 2026, the rise of RISC‑V hosts paired with Nvidia GPUs connected via NVLink is a real productive path — but it demands a disciplined operational approach across kernels, drivers, orchestration and networking.

Executive summary — what you must do first

Most important: treat a RISC‑V + NVLink GPU rack like a new platform family. Start with these four actions before deploying workloads:

Pin a supported kernel/firmware + board support package (BSP) and build checkpoints for rollback.
Validate vendor‑supplied NVLink Fusion and GPU driver bundles on a test bench; automate cross‑compilation and module signing.
Extend orchestration (Kubernetes/Slurm) with topology awareness so pods/jobs get NVLink locality guarantees.
Design the network fabric for hybrid traffic: NVLink intra‑GPU traffic, RDMA/RoCE/400GbE for inter‑node gradients and storage IO.

The 2026 context: why this matters now

In late 2025 and early 2026 the ecosystem shifted: SiFive announced integration work around Nvidia's NVLink Fusion for RISC‑V silicon, and multiple vendors are shipping RISC‑V server boards and BSPs tailored for accelerated workloads. That combination is making real heterogeneous clusters plausible — but operational gaps remain: kernel ABI churn, vendor driver packaging, and orchestration that understands NVLink fabrics.

The practical implication: teams who adopt RISC‑V + NVLink early will capture cost and power advantages, but only if they automate kernel/drivers, and build topology‑aware schedulers and networking practices upfront.

Hardware and topology: understanding NVLink Fusion on RISC‑V hosts

NVLink is not just a fast bus — it becomes a fabric when used with NVLink Fusion. On a RISC‑V host the important considerations are:

Physical topology: map NVLink bridges, GPU slots and host PCIe/NVLink host adapters into a topology database (node, chassis, GPU index, NVLink domain).
I/O boundaries: some RISC‑V SoCs expose NVLink through a PCIe‑like root complex requiring special device tree or ACPI entries; collect BSPs and DT overlays from your board vendor.
Peer access: NVLink improves intra‑node GPU bandwidth dramatically; NVLink Fusion aims to extend that low latency across nodes through fabric switches — treat that fabric as a separate low‑latency tier in your scheduler and monitoring.

Kernel and firmware: build, configure, and sign for production

Kernel and firmware are the foundation. Expect to spend engineering cycles here: building kernels for riscv64, enabling IOMMU/VFIO, ensuring device tree entries for NVLink host bridges, and supporting vendor firmware/OP‑ROM for NVLink endpoints.

Required kernel features

PCI/PCIe host controller support for the host SoC (CONFIG_PCI, CONFIG_PCIE_RISCV_* if vendor provides it).
IOMMU (CONFIG_IOMMU_API) and VFIO (CONFIG_VFIO) — essential for secure DMA isolation and GPU passthrough.
DMA mapping for coherent peer access and memory hotplug if using MIG and partitioned GPU instances.
VFIO/PCI STUB or vendor SR‑IOV support if the NVLink host adapter presents virtual functions.
Kernel livepatch/DKMS support to allow safe rolling of driver updates.

Cross‑compile and build checklist (practical)

Use this pattern in CI to ensure reproducible kernels and modules:

# set up toolchain and environment
export ARCH=riscv
export CROSS_COMPILE=riscv64-linux-gnu-

# obtain kernel source and apply vendor patches
git clone --depth 1 https://example.org/linux.git -b v6.x-bsp
cd linux
# apply BSP patches provided by board vendor
git am ../patches/*.patch

# kernel config fragment (append to default)
scripts/kconfig/merge_config.sh .config my_riscv_gpu_fragment.cfg

# build and package
make -j$(nproc)
make modules_install INSTALL_MOD_PATH=/tmp/kernel-root
make install INSTALL_PATH=/tmp/boot

Key tips: pin the kernel version to an LTS and keep a backport branch for vendor patches. Use small, auditable BSP patches rather than large monolithic changes.

Module signing & Secure Boot

For production clusters enable module signing and integrate it into your CI. On RISC‑V systems you may use OpenSBI + UEFI; configure a private key and sign every kernel module (including vendor GPU modules) before deploy.

# sign modules
scripts/sign-file sha256 private_key.pem public_key.der $(modinfo -n nvidia)

Drivers: vendor bundles, DKMS, and stability strategies

Driver support is the riskiest piece. In 2026 you should expect vendor driver bundles targeted for RISC‑V (GPU runtime, NVML/DCGM, kernel module). Your operational plan should include building, testing, and automating installation for those bundles.

Driver lifecycle best practices

Obtain a vendor‑signed driver bundle for riscv64 when available. If the vendor publishes kernel modules in source form, include them in your build pipeline.
Use DKMS or your distribution packaging to rebuild kernel modules automatically when kernels change; include a preflight test that loads the module and runs device discovery.
Run a canary fleet: dedicate a small cluster to new driver builds and smoke test with real workloads (training step, inference pipeline, multi‑process GPU access) before rollouts.
Keep a fallback image with a known‑good kernel + driver for rapid rollback.

Sample module build pattern

# vendor provides driver source in ./nvidia-driver
cd nvidia-driver
make KERNEL_DIR=/path/to/built/kernel ARCH=riscv CROSS_COMPILE=riscv64-linux-gnu-
# package into .deb or RPM to deploy via your repo

Orchestration: topology-aware scheduling and device plugins

A big operational blind spot is scheduling. NVLink's benefits are only realized when the orchestrator places workloads according to GPU adjacency and NVLink domains. Off‑the‑shelf GPU scheduling (simple counts of gpus) is not enough.

Kubernetes patterns

Device Plugin extension: extend the Nvidia device plugin to export NVLink topology metadata (NVLink domains, affinity groups, peer lists). The plugin should expose resources like nvidia.com/gpu:1 plus labels such as nvlink-domain=chassis-01 and nvlink-adjacent=gpu0,gpu1.
Custom scheduler or scheduler extender: implement a scheduler extender that reads the device plugin topology and prefers pods whose GPUs are in the same NVLink island.
Topology Manager and Topology Aware Scheduling: enable Kubelet's Topology Manager and integrate NodeResourceTopology CRD so pods can be scheduled with NUMA/NVLink locality in mind.
Pod spec example (topology‑aware):

apiVersion: v1
kind: Pod
metadata:
  name: nvlink-aware-job
spec:
  containers:
  - name: trainer
    image: my-ai-image:latest
    resources:
      limits:
        nvidia.com/gpu: 2
    env:
      - name: NVLINK_AFFINITY
        value: "prefer"
  nodeSelector:
    node.kubernetes.io/arch: riscv64
  tolerations:
    - key: nvlink-domain
      operator: Exists

Batch schedulers (Slurm)

For HPC-style workloads use Slurm GRES and a topology plugin that maps NVLink domains to GRES resource names (gpu:chassis01:gpu0). Use gres.conf and node features to control placement.

Networking: NVLink fabric + inter‑node RDMA and fabric design

NVLink and NVLink Fusion change the networking balance: intra‑node communication shifts to NVLink, but inter‑node gradients, model sharding and dataset IO still rely on your cluster fabric. Design around three tiers:

NVLink tier — low latency/high bandwidth inside NVLink domains and, with Fusion, across fabric switches; used for peer‑to‑peer GPU traffic when possible.
RDMA tier — InfiniBand or RoCE over 100/200/400GbE for inter‑node gradient exchange and high throughput storage paths.
Control plane — standard Ethernet for orchestration, monitoring and management.

Practical networking checks

Enable jumbo frames and tune MTU across RDMA fabric to match RoCE needs (e.g., 9216 bytes).
Enable PFC (priority flow control) and ECN if using RoCE to avoid packet drops under congestion.
Segment control plane on a separate management VLAN with restricted access and BGP/EVPN overlay for tenant traffic if needed.
Expose NVLink fabric stats: NVML/DCGM often exposes topology and link utilization. Map those metrics into Prometheus for scheduling heuristics.

Security and compliance: IOMMU, module signing and attestation

Security is non‑negotiable in mixed architecture clusters. Follow these practical rules:

IOMMU enforced: enable IOMMU for DMA isolation—vital for multi‑tenant GPU sharing and protection against DMA attacks.
Signed modules: sign all kernel modules and integrate with secure boot flows (UEFI/OpenSBI combinations) to prevent unauthorized drivers.
Node attestation: use TPM‑based attestation for node identity; include firmware and module hashes in attestation policy and tie to your compliance processes.
Least privilege: run GPU workloads in containers with minimal capabilities; avoid mounting /dev directly when you can use mediated devices (MDEV) or VFIO with careful controls.

Automation & CI: building repeatability into kernel and driver delivery

The single biggest operational leverage is automation. A reproducible pipeline reduces the time to validate new kernel/driver combos and rollback when issues appear.

Pipeline checklist

Source control for kernel and BSP patches. Use a declarative manifest for kernel+driver versions.
Cross‑compile and build artifacts in CI for every change; run unit, integration and smoke GPU tests on a hardware pool (canary nodes).
Artifact signing and immutable storage (artifact registry) for kernel images and driver packages.
Automated deployment runbooks: preflight checks (module loads, nvlink topology discovery), canary rollout strategy, and automated rollback on health degradations.

Example GitLab CI job (concept)

stages:
  - build
  - test
  - package

build-kernel:
  stage: build
  script:
    - ./scripts/build-kernel.sh --arch riscv64
  artifacts:
    paths:
      - out/kernel-*.img

smoke-test:
  stage: test
  script:
    - ./tests/smoke-nvlink.sh --node canary1

Monitoring and troubleshooting: metric sources and rapid diagnostics

Observability is both hardware and software: collect NVLink, GPU, and network metrics and correlate them to scheduling and job performance.

Key telemetry sources

NVML / DCGM (or vendor equivalent) exposes GPU utilization, NVLink link stats, and topology. If vendor DCGM supports riscv64 use it; otherwise expose sysfs counters or vendor telemetry agents.
Prometheus exporters on nodes (node_exporter, dcgm-exporter) and a dedicated GPU exporter for NVLink metrics.
Network telemetry from RDMA and switch counters (InfiniBand SM, SNMP on fabric switches, sFlow/telemetry) for cross-correlation.

Troubleshooting checklist

Start with kernel logs: dmesg for IOMMU faults, PCI errors, NVLink link failures.
Check driver module status: lsmod, modinfo, and vendor diagnostic utilities (e.g., nvidia-smi topo -m or equivalent).
Verify device tree/ACPI mappings for NVLink host bridges; mismatches between firmware DT and kernel drivers are common early blockers.
Use small synthetic workloads (peer bandwidth tests, latency microbenchmarks) to rapidly isolate whether problems are NVLink, PCIe root complex, or driver related.

Case study (operational example)

A mid‑sized AI provider piloted a 64‑node riscv64 cluster in Q4 2025. They ran into three repeatable failures during first driver rollouts: missing device tree NVLink nodes, unsigned modules rejected under their secure boot policy, and scheduler misplacement that forced cross‑node communication over 400GbE instead of NVLink.

Their fixes were systematic and instructive:

Added a DT overlay step in their image builder that injected vendor NVLink host bridge nodes based on board serials.
Integrated module signing into CI; all artifacts were signed and verified on boot through their attestation flow.
Extended the Kubernetes device plugin to publish NVLink adjacency and implemented a scheduler extender that preferred same‑island GPUs, improving intra‑job throughput by 2.6x and cutting cross‑node traffic by 48%.

Checklist before you deploy

Pin kernel + BSP and test driver builds on a hardware bench.
Automate cross‑compile and module signing in CI; keep signed artifacts in immutable registry.
Expose NVLink topology to your orchestrator and prefer NVLink locality for GPU placement.
Design network tiers: NVLink for intra‑node, RDMA for inter‑node, separate control plane VLANs.
Enable IOMMU, VFIO and secure boot; validate attestation and rollback flows.
Instrument NVLink/GPU/network metrics and validate remediation runbooks.

Future predictions (operational planning through 2027)

Expect faster vendor integration in 2026–2027: more native RISC‑V driver packages, upstream kernel patches for NVLink host support, and richer orchestration primitives for fabric‑aware scheduling. Operationally, teams that standardize their CI for kernel/driver pipelines and treat NVLink topology as a first‑class scheduling input will realize the greatest cost and performance advantages. Also consider carbon‑aware caching and power-aware scheduling as part of long‑term planning.

"Treat NVLink Fusion on RISC‑V as a new fabric: automate at the kernel and driver layer, and teach your scheduler about topology — otherwise you’ll waste the hardware’s advantage."

Actionable takeaways (do this this week)

Build one canary RISC‑V node image with your target kernel + vendor BSP and validate module load and NVLink discovery.
Integrate module signing into your CI and store signed images in an immutable registry.
Extend your device plugin (K8s) or scheduler (Slurm) to surface NVLink adjacency metadata to the scheduler.
Provision a small test fabric with RDMA + NVLink Fusion (if available) and run an end‑to‑end training pipeline to measure real throughput gains and cross‑node traffic patterns.

Closing: next steps and resources

Deploying heterogeneous clusters with RISC‑V hosts talking to Nvidia GPUs via NVLink is achievable in 2026 — but only with deliberate engineering on kernels, drivers, orchestration and networking. Focus your first sprint on reproducible kernel/driver pipelines, topology‑aware scheduling and network design that preserves NVLink's benefits.

Want a curated checklist and reusable CI templates to accelerate your rollout? Download our operational playbook or contact our team for a hands‑on workshop to adapt this guide to your hardware and workloads.

Call to action

Get the playbook: request the RISC‑V + NVLink operational pack (kernel configs, device plugin sample, CI job templates, and monitoring dashboards) to speed your deployment. Test an image with our checklist this week and cut your GPU network overhead while increasing throughput.

net work

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