Train the draft model for your workload

Custom Speculator Training, launched by Nebius Token Factory, allows teams to train workload-specific draft models using their own data, offering a significant improvement over generic models in speculative decoding. This approach enhances throughput and latency by aligning the draft model with the specific traffic patterns of a product, thereby increasing acceptance rates and reducing latency variability under load. The platform provides tools for data preparation, training, and deployment, with preset hyperparameters for ease of use and advanced options for those needing fine-tuning. This innovation is particularly beneficial for teams handling consistent, high-volume workloads, such as AI product teams, enterprise accounts already using speculative decoding, and ML engineers managing open-source models in production. It promises better unit economics and serving behavior control, making it an attractive option for those with predictable workload patterns. The launch is built on ongoing research and is integrated into an existing infrastructure that has been maturing over several months, with a focus on improving acceptance rates and shaping model behavior based on real-world data.

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