Why Is RISC-V Emerging as a Strategic Foundation for AI Custom Silicon?

As AI workloads scale across training, inference, and edge deployment, compute efficiency has become a board-level concern. Analyst estimates from McKinsey indicate that custom silicon can deliver 20–40% improvements in performance per watt compared to general-purpose processors, while materially lowering long-term total cost of ownership. At the same time, AI models are evolving faster than traditional processor roadmaps. This combination is pushing organisations to reassess not only what silicon they deploy, but how much control they retain over its architecture. 

Within this shift, RISC-V has moved from an experimental alternative to a strategically relevant instruction set, enabling AI-focused customisation without the economic and structural constraints of proprietary architectures. 

The Architectural Pressures Created by AI Workloads  

AI workloads introduce constraints that fundamentally differ from those in classical compute optimisation. Memory bandwidth, data locality, vector operations, and energy efficiency dominate outcomes, particularly for inference and edge scenarios. Proprietary instruction set architectures were optimised for broad compatibility and stable software ecosystems, which limits the degree of domain-specific tailoring that can be achieved without escalating licensing complexity or roadmap dependency. 

Hyperscalers exposed these limits early. Google’s Tensor Processing Units were developed to address inefficiencies in general-purpose CPUs and GPUs for neural network workloads. While TPUs remain proprietary, they illustrate a broader market reality: competitive AI performance increasingly depends on architectural specialisation rather than incremental improvements in general compute. 

For enterprises and platform providers without hyperscale economics, proprietary instruction sets introduce long-term cost exposure and reduced design freedom. This is the gap RISC-V addresses. 

Why Open Instruction Sets Matter for Custom AI Silicon  

RISC-V’s relevance lies in its modular, open specification, which allows instruction sets to be extended or streamlined based on workload requirements. For AI silicon, this enables tight coupling between custom accelerators, vector extensions, and memory hierarchies without negotiating royalties or depending on external ISA roadmaps. 

According to RISC-V International, global RISC-V core shipments exceeded 10 billion units by 2023, with data centre, automotive, and AI-oriented designs representing the fastest-growing segments. This growth reflects commercial deployment rather than academic experimentation. 

While the absence of licensing fees is often highlighted, the deeper strategic value is architectural sovereignty. As AI models, frameworks, and deployment patterns evolve, the ability to adapt silicon at the instruction level reduces innovation latency and strategic dependency. 

Adoption Across the Silicon Value Chain  

RISC-V adoption spans established leaders, platform providers, and specialised silicon firms, reinforcing its maturity. 

NVIDIA, headquartered in Santa Clara, has publicly disclosed the use of RISC-V cores within GPU management and control subsystems. Although NVIDIA’s AI accelerators remain proprietary, RISC-V enables internal flexibility and reduces reliance on third-party IP for non-differentiating components. Its presence inside market-leading AI platforms signals production-grade confidence. 

Intel has expanded its RISC-V engagement through Intel Foundry Services, positioning open instruction sets as a foundation for customers designing custom AI accelerators. This reflects a broader industry recognition that future differentiation will be customer-defined rather than vendor-dictated. 

SiFive, a California-based RISC-V semiconductor company, provides configurable cores optimised for performance and power efficiency. Its designs are deployed across AI inference, automotive, and infrastructure environments where energy efficiency and workload specificity directly affect commercial viability. 

Across these cases, RISC-V is not displacing proprietary architectures wholesale. It is enabling targeted customisation where control, efficiency, and cost discipline matter most. 

Edge AI and the Economics of Specialisation  

The strategic case for RISC-V strengthens further at the edge. AI inference in industrial systems, consumer devices, and smart infrastructure operates under strict power, latency, and thermal constraints. General-purpose architectures often carry unnecessary complexity for these narrowly defined workloads. 

Alibaba Group’s semiconductor subsidiary, T-Head, has deployed RISC-V-based processors across IoT and edge AI applications supporting logistics, smart cities, and industrial systems. The company has cited flexibility and ecosystem control as primary drivers, particularly for adapting silicon to region-specific deployment requirements. 

In such environments, the ability to remove unused instructions, integrate domain accelerators, and optimise memory paths directly impacts unit economics and deployment scale. 

Software Ecosystem Readiness for AI  

Concerns around software maturity have historically constrained RISC-V adoption. That constraint is rapidly eroding. Linux kernel support is production-grade, and major AI frameworks, including TensorFlow and PyTorch, now support RISC-V targets through LLVM and GCC toolchains. 

Enterprise Linux providers such as Canonical and Red Hat have expanded RISC-V support, signalling confidence in its operational viability. For AI workloads, this reduces friction across development, deployment, and lifecycle management, aligning RISC-V with enterprise expectations rather than experimental use cases. 

As AI toolchains become increasingly architecture-agnostic, software readiness is shifting from a barrier to an accelerant. 

Strategic and Geopolitical Considerations 

Beyond performance and cost, RISC-V carries strategic implications for supply-chain resilience and technological sovereignty. Governments and enterprises seeking reduced dependency on foreign IP licensors increasingly view open instruction sets as strategic infrastructure. 

India’s Ministry of Electronics and Information Technology has publicly endorsed RISC-V as part of its domestic semiconductor strategy. At the same time, European initiatives have positioned open architectures as foundations for sovereign compute projects. These moves reflect a convergence of technical, economic, and policy priorities around control and resilience. 

For enterprises, this translates into reduced vendor lock-in, more substantial negotiating leverage, and long-term architectural optionality. 

Conclusion: Control as the New Source of Advantage 

The adoption of RISC-V in AI-focused custom silicon reflects a more profound strategic shift. As AI workloads diversify and efficiency becomes a defining constraint, control over instruction sets, data movement, and architectural evolution is emerging as a competitive advantage. 

Custom silicon is becoming a default path for serious AI differentiation. Open instruction sets make that path accessible beyond hyperscalers, enabling a broader set of organisations to innovate at the silicon layer. 

In an environment where compute architecture increasingly shapes economic outcomes, RISC-V allows AI leaders to align hardware evolution with workload realities rather than external roadmaps. That alignment, more than cost reduction alone, defines its strategic case.