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Build an NVLink Interconnect Lab: Hardware, Firmware, and Software Stack for RISC‑V + GPUs

UUnknown

2026-02-07

10 min read

Hands‑on 2026 guide to prototype NVLink Fusion between SiFive RISC‑V boards and Nvidia GPUs—hardware, firmware, drivers, and debugging for lab validation.

Build an NVLink Interconnect Lab: Prototype NVLink Fusion between SiFive RISC‑V Boards and Nvidia GPUs

Hook: Tired of theoretical heterogeneous compute and vendor PPTs when evaluating heterogeneous compute? In 2026, the sensible way to validate NVLink Fusion between RISC‑V hosts and Nvidia GPUs is to build a hands‑on lab. This guide shows you how to procure parts, flash firmware, assemble the driver stack, and debug real interconnect problems so you can evaluate performance, software integration, and migration risks before committing production resources.

Why build an NVLink interconnect lab in 2026

Late 2025 and early 2026 saw major momentum: SiFive announced integration plans for Nvidia's NVLink Fusion infrastructure, and the ecosystem is quickly moving from concept to silicon samples and reference firmware. For teams evaluating heterogeneous AI stacks or planning vendor‑agnostic acceleration, a small lab lets you test:

End‑to‑end latency and bandwidth across RISC‑V hosts and Nvidia accelerators.
Firmware and bootloader interactions when exposing NVLink endpoints on a non‑x86 host.
Driver portability and how much vendor glue you must accept.
Migration and vendor‑lock risks before large‑scale procurement.

SiFive's NVLink Fusion integration announcement in late 2025 reframed the conversation about RISC‑V in AI datacenters; this lab is how you validate that reframing technically and operationally.

What you'll build — lab overview

The lab objective: attach an NVLink‑capable Nvidia GPU to a SiFive NVLink endpoint (development silicon or evaluation board), exercise the interconnect, and validate data movement via CUDA and GPUDirect. The lab comprises hardware, firmware, OS/kernel builds, Nvidia drivers, and test tools.

High‑level block diagram

RISC‑V board (NVLink endpoint) <==NVLink cable/mezzanine==> Nvidia GPU (NVLink‑enabled card) — control plane via serial/Ethernet to both devices — optional PCIe root complex or bridge depending on silicon.

Hardware procurement checklist

SiFive NVLink sample board or evaluation kit: request vendor samples that explicitly advertise NVLink Fusion endpoint capability. These will often be silicon evaluation boards rather than general developer boards.
Nvidia GPU with NVLink connectors: data‑center GPUs like A100/H100 family (or later 2025/2026 cards) that provide NVLink links and SDK support for NVLink Fusion features.
NVLink bridge/cable or mezzanine connector compatible with the GPU and SiFive board — the vendor usually supplies the correct mechanical connector or specifies the mezzanine pinout.
Power delivery: a PSU sized for target GPU peak power (often 300–700W for datacenter GPUs) and a stable DC source for the SiFive board.
Debug adapters: JTAG debugger (OpenOCD compatible), USB‑to‑serial cable for console logs.
Host workstation (x86_64 or ARM64) for cross‑compiling kernels, running CUDA tools, and controlling experiments.
Network switch for remote access and transfer between host and device.
Thermal solution: GPU heatsink or blower, extra fans for the lab.

Procurement notes and vendor coordination

Order evaluation silicon early. NVLink‑enabled RISC‑V samples are likely limited in 2026 and may require NDAs.
Confirm mechanical compatibility — NVLink connectors are not standardized across all new boards; confirm pinout and voltage domains with vendors.
Request firmware blobs and SDKs (NVLink Fusion SDK, vendor bootloader patches) from SiFive and Nvidia; expect early firmware to be under restrictive licensing. For legal and compliance checks see regulatory due diligence guidance before accepting proprietary blobs.

Firmware and bootloader: RISC‑V side (step‑by‑step)

On RISC‑V the boot flow and device tree are critical. You will typically chain OpenSBI -> U‑Boot -> Linux (or a minimal OS). For NVLink Fusion you must make sure the board presents a correct PCIe/NVLink endpoint and that the kernel has the right bindings.

1) Prepare the toolchain

Install a riscv64 Linux toolchain on your workstation.

apt install gcc-riscv64-linux-gnu gcc-riscv64-unknown-elf build-essential

2) Flash OpenSBI and U‑Boot

Get vendor patches for OpenSBI and U‑Boot. Steps are typically:

Cross‑compile OpenSBI for your SoC and flash via vendor tool or JTAG.
Build U‑Boot with the board config and enable PCI/PCIe discovery support and firmware loading hooks for NVLink.
Verify serial console output and boot into U‑Boot prompt.

make CROSS_COMPILE=riscv64-unknown-linux-gnu- PLATFORM=your_platform
# Use vendor programmer to flash: vendor_flash_tool --write opensbi.bin

3) Device tree and NVLink firmware

Ensure the device tree has a PCIe controller node describing the NVLink endpoint. Vendor firmware may include an NVLink microcontroller blob you must place in /lib/firmware or flash as part of the board firmware.

Confirm the PCIe controller registers are reachable from the kernel.
Install vendor NVLink firmware: copy firmware blobs to the rootfs and add device tree overlays if required.

Kernel and driver stack

On the RISC‑V host you'll build a Linux kernel with PCIe endpoint support, any vendor NVLink driver extensions, and enable VFIO if you plan to pass devices to userland. On the GPU side, run a supported Nvidia driver stack.

1) Build the kernel for RISC‑V

git clone --depth=1 https://github.com/torvalds/linux.git
cd linux
make ARCH=riscv CROSS_COMPILE=riscv64-unknown-linux-gnu- defconfig
# enable CONFIG_PCI, CONFIG_PCI_ENDPOINT, CONFIG_VFIO and vendor patches
make -j$(nproc) ARCH=riscv CROSS_COMPILE=riscv64-unknown-linux-gnu-

Apply vendor patches for NVLink Fusion if provided. These patches often add PCIe endpoint ID, DMA ranges, and device tree nodes.

2) Nvidia drivers and SDK

The GPU will normally run the standard Nvidia kernel module on a supported platform (x86_64 or 64‑bit host adapter). For NVLink Fusion testing you will need:

Nvidia Linux driver matching the GPU and CUDA version.
CUDA toolkit and samples (to run bandwidth and kernel tests).
NVLink Fusion SDK or vendor utilities supplied to enable or monitor the NVLink fabric.

3) GPUDirect and RDMA

If you need direct memory access from the RISC‑V host to GPU memory, enable GPUDirect RDMA and install nv_peer_mem and the RDMA drivers on the GPU side. This often involves:

Installing the Mellanox/DOCA/NVIDIA RDMA stacks if used in your environment.
Enabling the kernel modules on both sides: nv_peer_mem, ib_core, mlx5_core, etc.

Integration steps: wiring, enumeration, and bring‑up

Follow a deterministic bring‑up checklist to reduce unknowns.

Connect serial consoles for the SiFive board and GPU host so you can see early boot logs.
Attach NVLink bridge/cable. For mezzanine routes confirm seating and pin integrity.
Power the SiFive board and confirm U‑Boot boots; inspect PCIe enumeration in U‑Boot if available.
Boot the RISC‑V Linux kernel and check for PCIe devices:
```
lspci -vv -nn
```
You should see the NVLink endpoint or the GPU's PCIe function depending on topology.
On the GPU host, run
```
nvidia-smi topo -m
```
and
```
nvidia-smi
```
to verify the GPU is healthy and view interconnect topology.

Debugging tips and common failure modes

Early prototypes will fail in a few predictable ways. Here are pragmatic debug strategies borrowed from hands‑on lab experience.

1) No PCIe enumeration

Check power rails and PERST signals. Many NVLink/PCIe edge cases are due to reset lines or missing rails.
Inspect device tree PCIe node. Ensure resource windows (mem/IO ranges) are set so the Root Complex can map BARs.
Use the JTAG to read controller registers and confirm link training. See field kit recommendations for reliable debug adapters.

2) Link comes up but performance is poor

Check negotiated link width and speed with lspci -vv and dmesg. If link negotiates to x1 or Gen1, look for SFP/connector problems or PHY configuration mismatches.
Disable ASPM temporarily to eliminate power‑management effects on link speed.
Run a microbenchmark like CUDA samples bandwidthTest to measure effective throughput.

3) Kernel oops or driver crashes

Collect dmesg, kernel oops logs, and serial console trace. Use
```
journalctl -k
```
where available.
Enable kernel debug options (PCIe/endpoint debug, dma debug) in a development kernel and re‑test.
Use perf and ftrace to narrow the call path where faults occur.

4) GPUDirect RDMA failures

Confirm the device IOMMU mapping and IOVA windows. RDMA requires consistent physical addressing and compatible IOMMU settings across the fabric.
Check nv_peer_mem logs and the RDMA stack diagnostics (ibv_devinfo).

Useful commands

```
lspci -vv -nn
```
(PCI device enumeration)
```
dmesg | grep -i pci
```
(PCI boot messages)
```
nvidia-smi topo -m
```
(NVLink topology)
```
ibv_devinfo
```
(RDMA device info)
```
cat /proc/iomem
```
(verify memory maps and BAR regions)

Validation and benchmark suite

Once the stack is stable, run a battery of tests to validate the interconnect and software behavior.

Connectivity: lspci, nvidia‑smi topo, rdma device enumeration.
Functional: CUDA sample "vectorAdd", a tiny kernel launched on the GPU triggered by the RISC‑V host if applicable.
Bandwidth: bandwidthTest from CUDA samples for host<->device and peer‑to‑peer tests across NVLink.
RDMA/GPUDirect: run ibv_rc_pingpong and rdma_bw tests against GPU memory to measure end‑to‑end raw throughput and latency.
Stress: sustained kernel launches and memory transfers for hours to validate thermal and link stability.

# Example: run CUDA bandwidth test on the GPU host
cd ~/NVIDIA_CUDA-xx/samples/1_Utilities/bandwidthTest
make
./bandwidthTest --memory=pinned --mode=range

Case study: prototype timeline and effort estimate

Here is a realistic timeline for a small team (2 engineers) to build a working prototype in 2026:

Week 1–2: Procurement and vendor engagement (obtain SiFive sample, Nvidia card, cables, PSU).
Week 3–4: Flash bootloaders, build and boot a Linux image, and validate the basic board bring‑up with serial console logs.
Week 5–7: Kernel and driver integration — apply vendor patches, enable PCIe endpoint, and cross‑compile kernel.
Week 8–9: Install Nvidia drivers on the GPU host, run basic CUDA tests, and verify NVLink physical layer and enumeration.
Week 10–12: End‑to‑end GPUDirect and performance testing, debugging, and documentation.

Allow extra time for NDA negotiations or for proprietary firmware drops; early SiFive/Nvidia samples in 2026 may require vendor support to reach full functionality. For checklisting and reducing tool fatigue, see our recommended tool audit.

Advanced strategies and future‑proofing

Once you have a working prototype, consider these advanced topics to scale your lab efforts to production‑grade evaluation.

Virtualization and SR‑IOV: evaluate how NVLink endpoints behave under VFIO and whether the vendor exposes SR‑IOV functions for multiple guests. See guidance on edge developer workflows when testing virtualized setups.
CI/CD for firmware and kernel builds: automate kernel builds, firmware flashes, and smoke tests via GitHub Actions or an on‑prem runner to reproduce results quickly — a pattern covered in our edge-first developer playbook.
Observability: wire in telemetry (perf counters, NVSwitch counters if available) to capture link utilization over time — combine this with an edge auditability plan to keep traceability across firmware changes.
Security: review the attack surface of firmware blobs and proprietary drivers; sandbox user‑space components and use signed firmware where possible.
Migration plan: test moving workloads from an x86 NVLink topology to the RISC‑V host under realistic VM/containers to measure overhead and porting effort — an on‑prem vs cloud decision matrix can help scope tradeoffs (on‑prem vs cloud).

Practical takeaways — a checklist for launch

Secure vendor evaluation samples and firmware early (expect lead time and NDAs).
Prepare a cross‑compile toolchain and automated kernel build script to iterate quickly.
Keep serial and JTAG connections handy for low‑level debugging.
Use standardized benchmarks (CUDA samples, RDMA tests) to quantify results and regressions.
Document every firmware version, kernel revision, and driver build — reproducibility matters when vendors release patches. See our tool and recordkeeping guide.

Final thoughts and 2026 outlook

In 2026, NVLink Fusion between RISC‑V hosts and Nvidia GPUs is shifting from announcement to practical evaluation. Early adopters who invest in a small lab will be able to quantify the operational and technical tradeoffs — from firmware complexity to RDMA behavior and driver compatibility — before committing to large‑scale designs.

Expect vendor ecosystems to mature through 2026: more reference firmware, standardized device tree bindings, and richer monitoring tools. But early hands‑on labs remain the best way to expose edge cases, validate performance, and build the internal expertise needed to manage heterogeneous fabrics.

Call to action: Ready to prototype? Start with the procurement checklist above and set up a repeatable CI pipeline for kernel and firmware builds. If you want an opinionated checklist tailored to your environment (selected SiFive board, expected GPU model, and power constraints), reach out to the team or download our lab starter repo to get scripts, example device trees, and benchmark harnesses that speed you from unboxing to measured results. Also see our practical notes on edge developer workflows and reliable field kits to simplify bring‑up.

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