Generative language models face many challenges during the transition from training to practical applications. One major issue is how to achieve optimal performance during the inference stage. Current strategies, such as Reinforcement Learning from Human Feedback (RLHF), primarily focus on improving the model's win rate but often overlook decoding strategies during inference, such as Best-of-N sampling and controlled decoding. This gap between training objectives and actual usage may lead to inefficiencies and affect the quality and reliability of outputs. To address these issues, Google D

Deep learning researchers have found that the performance of language models in logical reasoning remains a significant challenge. Recent research reveals that the order of premises in tasks has a significant impact on the logical reasoning performance of language models. The findings may guide experts in their decisions when using language models for fundamental reasoning tasks.

Intel's Gaudi2 technology matches NVIDIA's AI accelerators in large-scale language model inference. Gaudi2 shows better inference performance than the NVIDIA A100, with higher memory bandwidth utilization. The cost-performance ratio for both training and inference exceeds that of the NVIDIA A100 and H100. New data confirms Intel's performance in large-scale language model inference. Gaudi3 technology is expected to be released in 2024, bringing significant performance advancements.

Research shows that Intel's Gaudi2 technology competes with NVIDIA's AI accelerators in large-scale language model inference. The inference performance of Gaudi2 is comparable to NVIDIA's H100 system in decoding and surpasses that of the A100. According to public cloud pricing, Gaudi2 offers better value for training and inference compared to NVIDIA's A100 and H100. Intel's Gaudi3 is planned for release in 2024, promising four times the processing power and double the network bandwidth. Intel is committed to advancing high-performance computing and