Segment 48

In this sub-session, the assistant restored Qwen3.6-27B deployment on CT129 to its high-performance 3-step NEXTN MTP configuration, achieving ~55 tok/s on realistic coding prompts and profiling the decode step to confirm an 83% memory-bandwidth-bound bottleneck near the theoretical ceiling for 2× A6000 hardware. For the DFlash drafter training, the assistant researched and implemented three key sample efficiency improvements: replacing hard-label cross-entropy with soft-label KL distillation loss, adding streak-aware dynamic loss weighting to focus on critical acceptance positions, and introducing a cosine-annealed noise schedule that transitions from regularization to precision. These changes were integrated into dflash_model.py and train_dflash_pipeline.py, tested, and prepared for a fresh training run. Additionally, Weights & Biases logging with graceful fallback was integrated into the pipeline, and all changes were documented in a comprehensive DEPLOY_V2.md deployment guide.

The Ceiling and the Lever: Profiling Memory-Bound Inference While Reinventing a Drafter's Training Objective 4017 words

Chunks

• The Two Frontiers: Deploying a 27B Model While Reinventing Its Drafter
  3149 words
• The Architecture of Observability: How 30 Lines of W&B Integration Completed a Multi-Phase DFlash Training Pipeline
  2582 words