B200 fine-tuning benchmark shows up to 5.8× speedup and 50% less VRAM
antoine_chaffin · x · 2026-07-28
- A benchmark on B200 shows that fine-tuning retrieval models with packed/optimized training can materially improve efficiency.
- GTE-ModernBERT-base + Sentence Transformers: 4.5× faster and 50% less VRAM.
- GTE-ModernColBERT-v1 + PyLate: 5.8× faster and 35% less VRAM with gradient cache.
- The attached charts compare loss curves, training time, and peak CUDA memory, suggesting the optimized setup preserves convergence while cutting training cost.
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