@asmah2107: Compression techniques I’d study if I wanted small but smart LLMs. 1.Quantization 2.Distillation 3.Low-Rank Adaptation …
Summary
A tweet listing 12 compression techniques for reducing LLM size while maintaining performance, including quantization, distillation, and low-rank adaptation.
View Cached Full Text
Cached at: 07/06/26, 04:15 PM
Compression techniques I’d study if I wanted small but smart LLMs.
1.Quantization 2.Distillation 3.Low-Rank Adaptation 4.Weight Sharing 5.Sparse Matrices 6.Layer Dropping 7.Knowledge Transfer 8.Embedding Compression 9.Mixed Sparsity 10. Progressive Shrinking 11.Structured Pruning 12.AutoML Compression
Follow @asmah2107 to update your game on LLM optimisations.
Similar Articles
@akshay_pachaar: LLM quantization techniques I'd learn if I had to fit a 70B model on a single GPU: (bookmark this) A 70B model in FP16 …
A Twitter thread explains five key LLM quantization techniques (RTN, GPTQ, AWQ, LLM.int8(), QAT) for fitting large models on limited hardware, and references a comprehensive study paper.
@divaagurlxw: Inference optimizations I’d study if I wanted sub-second LLM responses: 1.KV-Caching 2.Speculative Decoding 3.FlashAtte…
A tweet listing 16 inference optimization techniques for achieving sub-second LLM responses, including KV-caching, speculative decoding, FlashAttention, and various parallelism methods.
LLM Compression with Jointly Optimizing Architectural and Quantization choices
Researchers from UiT and University of Oslo propose a differentiable NAS framework that jointly optimizes architectural configurations and mixed-precision quantization for LLM compression, achieving up to 1.4× faster inference or 6% higher accuracy across seven reasoning tasks compared to sequential NAS-then-quantization baselines.
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
A novel end-to-end framework for LLM compression that jointly optimizes structural pruning and mixed-precision quantization, achieving significant perplexity reductions and speedups over state-of-the-art methods, especially at ultra-low bit precisions.
Break Through the Compression Bottleneck: From Theory to Practice
This paper provides the first mathematical proof that low-rank decomposition and quantization are non-orthogonal when combined for LLM compression, leading to performance degradation, and proposes a novel Diagonal Adhesive Method (DAM) to mitigate this loss.