Datacenter Efficiency Optimization
Efficiency mining for existing clusters: model-switch effective TPS, concurrent loading, checkpoint writes, utilization modeling — improve output first, add GPUs later.
Core Capabilities and Measured Basis
- Measured modeling of model-switch effective token output: at 20 switches/hour, effective compute utilization rises from 46.7% to 62.8% (measured, R1)
- Concurrent model loading: 8 GPUs cold-reading simultaneously, 16% faster wall-clock and 30% faster per-model load (measured, R1)
- Training checkpoint concurrent writes 1.9× faster, shrinking training bubbles (measured, R1)
- HBM-equivalence methodology: an external KV tier can substitute for ~128 GB-class HBM residency (R5, 14B seven-way comparison)
Supporting Evidence
LMCache parallel-read patch, multi-GPU KV tiering scale-out, concurrent model loading, model-switch effective TPS, training checkpoint concurrent writes (Qwen2.5-32B/7B).
Download report PDF ↓Seven-way comparison (GDS direct read / two re-compute HBM tiers / low-HBM direct read); three-step HBM-equivalence argument. Platform: MetaX N260 single GPU — methodology portable across platforms, numbers not mixed with MI308X.
Download report PDF ↓The Engagement Path
Joint test first, decisions second: gate-based acceptance with built-in stop-loss. The full costing model is provided as reproducible Python after NDA — customers can rerun it with their own parameters. Every key figure on this site carries a report ID and is open to third-party verification.