From the 1 of 5 linked papers with an AI index.
5 papers
FastTPS: An Optimized Method for LLM Token Phase for AI accelerators
Wenzong Yang, Danyang Zhang, Kun Cao +17
FastTPS is a set of techniques that reduce memory traffic and fuse operations during the token‑phase of large language model inference on AI accelerators, achieving up to a 6× spee…
MINCE: Shrinking LLM Evaluation Datasets via Few-Model Monte Carlo Calibration
Devleena Das, Rajeev Patwari, Vikram Kumar Bukka +3
Evaluating LLMs across many model variants -- quantized, fine-tuned, or deployment-specific -- requires running large benchmarks repeatedly, a process that can take tens of hours p…
Ablation, Statistical Inference, and Validation for KV-Cache Compression
Paolo D'Alberto, Ashish Siarasao, Elliott Delaye +1
This study systematically compares Turbo-Quant and SpectralQuant KV-cache compression, evaluating non-dominated schemes, including WHT rotation with Beta Lloyd-Max and QJL, through…
Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data
Devleena Das, Rajeev Patwari, Elliott Delaye +1
Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degr…
KV Pareto: Systems-Level Optimization of KV Cache and Model Compression for Long Context Inference
Sai Gokhale, Devleena Das, Rajeev Patwari +2
Long-context Large Language Models (LLMs) face significant memory bottlenecks during inference due to the linear growth of key-value (KV) cache with sequence length. While individu…