TNG Technology Consulting GmbH has unveiled R1T2, a new variant of DeepSeek R1-0528 that operates 200% faster while maintaining high reasoning performance. With significant reductions in output token count and inference time, R1T2 is tailored for enterprise applications, offering an open-source solution under the MIT License.

DeepSeek's 3FS distributed file system benchmarks are analyzed through a "performance reality check" method that compares reported metrics against theoretical hardware limits. The analysis highlights potential bottlenecks in network and storage components, particularly focusing on an AI training workload, where network bandwidth was identified as the primary limiting factor despite impressive throughput figures. This approach aims to validate performance claims and guide optimization strategies before extensive benchmarking.

Strategies for deploying the DeepSeek-V3/R1 model are explored, emphasizing parallelization techniques, Multi-Token Prediction for improved efficiency, and future optimizations like Prefill Disaggregation. The article highlights the importance of adapting computational strategies for different phases of processing to enhance overall model performance.