✨Retentive Networks offer lower latency, higher throughput, and better scalability than Transformers.
🔥Retentive Networks achieve training parallelism and low-cost inference by making computations linear.
🧠Retentive Networks combine the advantages of both Transformers and recurrent networks.
💡Retentive Networks eliminate the need for the softmax operation, enabling parallel computations.
🚀Retentive Networks show promising results in experiments, but further research is needed to evaluate their full potential.