From the 1 of 8 linked papers with an AI index.
8 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…
Recover-LoRA: Data-Free Accuracy Recovery of Degraded Language Models via Low-Rank Adaptation
Devleena Das, Rajeev Patwari, Ashish Sirasao
Inference optimizations such as quantization, pruning, format and datatype conversion, model export, and serialization can lead to functional degradations in language model task pe…