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FastTPS: An Optimized Method for LLM Token Phase for AI accelerators
Wenzong Yang, Danyang Zhang, Kun Cao +17
The popularity of large language models (LLMs) escalates an ongoing demand for effective inference. However, due to the sequential processing of tokens during the token phase in de…
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…
Exploring the Dynamic Scheduling Space of Real-Time Generative AI Applications on Emerging Heterogeneous Systems
Rachid Karami, Rajeev Patwari, Hyoukjun Kwon +1
The integration of generative AI models, particularly large language models (LLMs), into real-time multi-model AI applications such as video conferencing and gaming is giving rise…