Rubytech has completed testing of the Russian large language model Cotype 3 from MWS AI on Chinese graphics processing units, demonstrating performance comparable to systems based on NVIDIA H100 accelerators.
The experiments were conducted on the Skala^r software-hardware complex designed for generative artificial intelligence workloads. In the test configuration, eight Chinese GPUs were deployed. When processing contexts of 27,000 tokens, the system achieved an average first-token generation time of approximately 8 seconds and inter-token latency ranging from 111 milliseconds.
According to Rubytech, these metrics are sufficient to support enterprise-grade AI services. Engineers performed extensive adaptations of drivers, the software stack, and orchestration mechanisms. In selected inference scenarios, these optimizations delivered speedups of 2 to 2.2 times relative to the default Chinese GPU environment.
The containerized architecture is expected to simplify model deployment and accelerate the transition from laboratory testing to production environments. The primary conclusion drawn from the trials is that corporate AI infrastructure can be constructed without exclusive dependence on NVIDIA accelerators.
This finding holds particular significance for government agencies, state-participated enterprises, and organizations operating critical information infrastructure that require localized model hosting, controlled infrastructure, and the ability to scale without supply-chain uncertainties associated with NVIDIA H100 GPUs.
Rubytech also indicated that Chinese GPUs can reduce total cost of ownership in specific use cases. The company stressed that the results represent an additional viable option rather than a full departure from NVIDIA technology. Future plans include testing newer generations of Chinese accelerators and expanding the range of supported Russian models and enterprise AI services.
