At the recent RISC-V China Summit, NVIDIA made a groundbreaking announcement that could reshape the computing landscape: CUDA will now fully support the RISC-V instruction set architecture. This news, confirmed through an official tweet by RISC-V International, quickly ignited widespread excitement across the global tech community.
Breaking Two Decades of x86/ARM Monopoly #
Since its debut in 2006, CUDA has been NVIDIA’s cornerstone for dominating the AI computing field, tightly bound to x86 and ARM architectures. Its ecosystem has been so robust that even AMD’s ROCm platform, despite years of development and the release of ROCm 7, has struggled to catch up in market adoption.
Now, with CUDA opening up to RISC-V, a significant shift is underway:
- Sovereignty in Technology: RISC-V CPUs can now serve as host processors managing CUDA workflows, replacing the previous dependence on x86 or ARM for control tasks.
- Cost Revolution: As an open-source, royalty-free ISA, RISC-V drastically reduces chip development costs—especially beneficial for Chinese companies and startups.
- Architectural Freedom: RISC-V’s modular design enables manufacturers to tailor architectures to specific needs, avoiding unnecessary “silicon bloat” and license constraints.
NVIDIA’s move isn’t a superficial port, but a system-wide ecosystem migration:
- Core Component Porting: CUDA Toolkit (compiler) and driver stack (KMD/UMD kernel drivers) are being prioritized to establish a functional runtime framework.
- Vertical Library Migration: Over 900 domain-specific CUDA libraries must be reengineered, spanning deep learning inference (e.g., FasterTransformer), EDA acceleration, scientific computing, and more.
- Third-Party Ecosystem Integration: AI frameworks like PyTorch will require re-deployment and tuning for RISC-V compatibility, ensuring a complete toolchain.
These transitions also face significant technical hurdles:
- RISC-V currently lacks a standard for Unified Virtual Memory (UVM), hampering efficient CPU-GPU data sharing.
- On the hardware front, no SoCs fully comply with the RVA23 server-class spec—Alibaba’s C920 dev board is usable but still falls short.
NVIDIA’s Ambition to Ditch the CPU #
NVIDIA’s embrace of RISC-V is no act of altruism—it’s a strategic move to reclaim control over the compute stack:
- Disrupting the CPU Duopoly: With x86 (Intel/AMD) losing control of host CPU tasks and ARM’s energy-efficiency claims under scrutiny, NVIDIA’s shift toward RISC-V tilts the trust scale.
- Opening the Door to China: RISC-V’s open-source model aligns perfectly with China’s goals for semiconductor independence. CUDA compatibility could spark a surge in domestic AI chips. If local SoCs can fully support AI through RISC-V + CUDA, who needs Western CPUs?
- Completing the NVLink Fusion Puzzle: In NVIDIA’s vision for heterogeneous computing, RISC-V CPUs will tightly integrate with in-house GPUs, DPUs, and network chips via NVLink—building a full-stack accelerated system to realize the “data center as a computer” concept.
Who Will Lead in the Post-x86 Era? #
This bold move has already triggered ripple effects. The x86 and ARM camps now face growing competitive pressure, especially in China and the edge computing market. For Chinese RISC-V chipmakers, CUDA support offers a passport into the AI server arena.
Meanwhile, NVIDIA, with CUDA’s now cross-architecture capability, is solidifying its position at the heart of the AI world. Regardless of whether the underlying CPU is x86, ARM, or RISC-V—if the GPU is NVIDIA, the ecosystem remains under its control.
When an open ISA like RISC-V converges with a dominant acceleration platform like CUDA, it signals the dawn of a new era in computing.