SpecQuant speeds up local language model responses using adaptive quantization
SpecQuant: Speculative Decoding with Multi-Parent Quantization for Adaptive LLM Inference
Machine Learning
Summary
Running big language models on home computers is tough because they need a lot of power and memory. The authors created SpecQuant, a way to make these models run faster without retraining by using different versions of the model that vary in precision. Easier questions use simpler versions, while harder ones use the full model, all while sharing the same base setup. This makes local AI faster by about 35-43% without losing much accuracy.
What this means in practice
- •For mobile app developers: Run language models quickly on smartphones by switching model precision based on user input complexity without extra training.
- •For edge computing teams: Deploy large language models on diverse local devices adapting resource usage dynamically for efficient inference.
Authors
Harish KB, Jagadeeswaran M, Pradheep P, Yuvanesh S, Sivakumar T
Abstract
Running large language models (LLMs) locally continues to be limited by restrictions of compute and memory on consumer hardware. The popular acceleration technologies, such as quantization, speculative decoding, and adaptive inferencing, offer substantial speed boosts but usually necessitate retraining, per architecture tuning, or draft models. SpecQuant is a trainingfree framework, that combines speculative decoding with multiparent quantization to perform adaptive, efficient inference of LLMs. SpecQuant derives multiple quantized variants (INT4, FP8, FP16) from a shared base model, and dynamically routes queries based on predicted complexity; lightweight variants are used for simple or factual tasks, and full-precision models are used for complex reasoning tasks or long-context inputs. The shared-weight design of SpecQuant ensures sufficient token acceptance for speculative decoding without compatibility issues using separate draft parent models. We evaluate SpecQuant on Qwen2.5 based models on the MMLU, AlpacaEval, and GSM8K datasets, or benchmarks, demonstrating 35-43% speedups without degrading accuracy greater than 2%, substantial within the LLM community. SpecQuant enables practical on-device LLM deployment across diverse hardware without special infrastructure or expertise.