RISC-V is an open instruction-set architecture (ISA), the software-facing rules processors follow, not a specific chip or AI accelerator. That matters because an AI system can use a RISC-V core to run software or coordinate tasks while separate hardware handles specialized calculations.
What RISC-V contributes to an AI system
RISC-V defines a base set of instructions and allows implementations to add standard or custom extensions. A processor core implements a base ISA and whichever extensions it supports; an accelerator is separate hardware designed to handle particular operations. A RISC-V platform can combine its cores with other types of cores, fixed-function accelerators, memory and interconnects.
That makes the architecture flexible for AI designs, but the RISC-V name alone says nothing about a product’s AI speed. The result depends on the actual core, extensions, accelerator, memory system and software.
The ratified V extension supports vector operations, which apply one instruction to multiple data elements. The application-processor profile specifies a minimum vector-register length, or VLEN, of 128 bits. Ratified BF16-related vector extensions specify conversions between BF16, a 16-bit floating-point format, and FP32, as well as a multiply-accumulate operation that widens BF16 inputs into FP32 results. These are defined capabilities of the extensions, not features guaranteed in every RISC-V processor.
RISC stands for “reduced instruction set computer.” RISC-V does not have one instruction count for the whole family: the RV32I base has 40 unique instructions, while the specification allows a count of 38 for a simple implementation under specific conditions. Optional extensions add their own instructions.
Where reported RISC-V AI applications fit
SpacemiT’s K1 was aimed at endpoint and edge computing, while the company’s K3 targets robotics, edge AI and intelligent hardware. Jiemian reported that K3 combines general-purpose CPU computing, AI inference and real-time control, and that its first mass-production delivery took place on April 30, 2026.
A report published by 21jingji on October 22 said the humanoid robot Linglong 2.0 completed the Beijing Yizhuang half-marathon in April 2026 using SpacemiT’s RISC-V-based K3. The report gives the month, not the exact day. It is a specific reported robotics example, alongside K3’s stated edge-AI and control roles.
SpacemiT announced its V100 server CPU at the 2026 RISC-V China Summit on October 19. The company also announced three server offerings: C2201 for general computing, G5201 for AI computing and S5236 for storage. NeoTeo’s earlier coverage of the V100’s specifications looks more closely at that processor.
These examples place RISC-V in edge devices, robotics and server systems, but they describe different stages: K3 had a reported mass-production delivery and a reported robot use, while V100 and its server offerings were announced at the summit. They do not supply a performance result for a particular AI workload.
Software support and chip supply also shape what a design can do in practice. A Securities Daily article republished on 163.com described software compatibility across vendor-specific extensions as fragmented. It also reported that some companies with orders for 60,000 wafers delivered only 20,000 to 30,000 wafers. Those figures describe the companies cited in that account, not the whole RISC-V industry.
How designers explore AI hardware before silicon
Chip designers can use virtual platforms, software models of a proposed system, to explore a design before committing to its register-transfer-level (RTL) implementation. In a RISC-V Summit 2020 presentation, Imperas presenters Simon Davidmann and Duncan Graham demonstrated running application binaries, profiling functions and instruction coverage, estimating timing, modeling custom instructions and debugging heterogeneous multicore systems.
