Post-training for production: Making open models reliable at scale
Moving open models to production requires more than accuracy; it demands predictable latency, stable behavior under load, and cost control. In this live session, we’ll walk through how leading teams use post-training as the missing layer between a promising checkpoint and a production-ready system.
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What you will learn
- How speculative decoding controls tail latency (P90/P99) in long-context and high-concurrency workloads
- Why generic draft models fail and how to post-train custom draft models on production data
- How fine-tuning, distillation, quantization, and speculative decoding stabilize inference at scale
- Deploying custom speculator pipelines via Nebius Token Factory API without infrastructure changes
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