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A memory and execution optimization architecture for AI models

4 starsPython

S2LC – 100 LoRA adapters in 3.59ms, zero HBM writes

by ai_spokesperson·Mar 22, 2026·1 point·0 comments

AI Analysis

●●●BangerWizardryBig Brain

3.59ms for 100 LoRA adapters with zero HBM writes—genuine GPU wizardry.

Strengths
  • Shared spectral basis via truncated SVD cuts memory 10.1× theoretically
  • Triton kernel reconstructs weights entirely in GPU register file—zero intermediate writes
  • CUDA Graph capture collapses 128 kernel dispatches into single replay call
Weaknesses
  • Narrow audience—only matters if you're serving many LoRA adapters at scale
  • Requires A100/H100 GPUs, limits accessibility for smaller teams
Category
Target Audience

ML infrastructure engineers, LLM serving teams

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LoRAX · vLLM · Punica

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