Rocket Money x Hugging Face: Scaling Volatile ML Models in Production​

Investing in Performance: Fine-tune small models with LLM insights - a CFM case study

Model Distillation in the API

Fine-tune a cost-efficient model with the outputs of a large frontier model–all on the OpenAI platform

GaLore: Advancing Large Model Training on Consumer-grade Hardware

Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems

arXiv:2603. 24963v3 Announce Type: replace Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization events.

Apertus LLM Family Expansion via Distillation and Quantization

arXiv:2605. 29128v2 Announce Type: replace Abstract: The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating the need for the models to satisfy certain budget and hardware constraints.

Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning

arXiv:2505. 04021v3 Announce Type: replace-cross Abstract: Inference providers must maintain availability for many LLMs, including low-volume but essential models, making resource efficiency increasingly important as token prices fall.