360Brew: LLM-based Personalized Ranking and Recommendation - Hamed and Maziar, LinkedIn AI
Jul 16, 2025 · 22:00
LinkedIn AI's Hamed Firooz and Maziar Sanjabi present BrewXL, a 150-billion-parameter foundation model for ranking and recommendation that personalizes the platform's feeds, jobs, and search. They show that training a large model then distilling to a 3B model outperforms training a small model from scratch. Scaling data, model size (from 7B to 8x22B), and context length all improve performance, though context beyond the trained length degrades accuracy. The model achieves zero-shot generalization on out-of-domain tasks, matching or beating task-specific production models, and significantly reduces the cold-start gap for users with fewer than five interactions. For serving, they combine gradual pruning, mixed-precision quantization (FP8 for most layers, FP32 for the LM head), and 4D attention masks to score up to 500 items without cross-attention, yielding a 7X latency reduction and 30X throughput increase per GPU.
