Modern AI hardware performance is increasingly limited by memory bandwidth and data movement rather than raw arithmetic power. Between 2012 and 2022, the 64-bit computing speed of NVIDIA GPUs increased approximately 80 times, while memory bandwidth grew only 17 times. This imbalance forces computational units to remain idle while waiting for data, making the entire system architecture more critical than individual chip performance.
Developing a universal GPU in Russia faces significant hurdles, including limited access to advanced manufacturing processes, HBM, and complex packaging technologies. Furthermore, the software ecosystem, such as CUDA, requires years of investment and continuous support. Experts suggest that a more viable strategy for Russian developers is to focus on specialized AI accelerators rather than attempting to replicate universal GPU platforms.
Specialized accelerators for inference, edge computing, industrial automation, and robotics offer a more realistic entry point. These applications prioritize latency, energy efficiency, and predictable response times over peak FLOPS. In these systems, RISC-V architecture can serve as a control core, a data movement controller, or part of a heterogeneous platform, allowing for optimized designs tailored to specific workloads.
The inference process, particularly the 'Decode' stage in transformer models, is heavily memory-bound as it involves generating tokens sequentially. Unlike training, which requires massive clusters, inference allows for hardware optimization based on specific data formats and model requirements. By narrowing the scope, developers can manage complexity and create cost-effective solutions that do not rely on the massive scale of general-purpose GPU clusters.
