GPTQ quantizes 175B-parameter GPT models to 3-4 bits per weight in one shot using approximate second-order information, achieving negligible accuracy degradation and 3-4x inference speedups.
Xing, Joseph E
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Alpa automates model-parallel training of large deep learning (DL) models by generating execution plans that unify data, operator, and pipeline parallelism. Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations. They do not suffice to scale out complex DL models on distributed compute devices. Alpa distributes the training of large DL models by viewing parallelisms as two hierarchical levels: inter-operator and intra-operator parallelisms. Based on it, Alpa constructs a new hierarchical space for massive model-parallel execution plans. Alpa designs a number of compilation passes to automatically derive efficient parallel execution plans at each parallelism level. Alpa implements an efficient runtime to orchestrate the two-level parallel execution on distributed compute devices. Our evaluation shows Alpa generates parallelization plans that match or outperform hand-tuned model-parallel training systems even on models they are designed for. Unlike specialized systems, Alpa also generalizes to models with heterogeneous architectures and models without manually-designed plans. Alpa's source code is publicly available at https://github.com/alpa-projects/alpa
representative citing papers
A framework called Voltron elastically distributes LLM inference across heterogeneous edge devices using layer-wise hybrid parallelism and mixed precision, achieving up to 16.5% higher accuracy than single-device execution while meeting QoS latency constraints.
RRFP introduces a readiness-driven runtime for pipeline parallelism that uses schedules as hints and ready-set arbitration to improve utilization under runtime variability, reporting up to 2.77x speedup on multimodal workloads.
Distilling step-by-step uses LLM-generated rationales as additional supervision in a multi-task framework so that 770M-parameter models outperform 540B-parameter models on NLP benchmarks with only 80% of the data.
A hybrid JIT-CUDA Graph framework reduces TTFT by up to 66% and P99 latency versus TensorRT-LLM for single-GPU LLaMA-2 7B inference on short prompts.
citing papers explorer
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GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
GPTQ quantizes 175B-parameter GPT models to 3-4 bits per weight in one shot using approximate second-order information, achieving negligible accuracy degradation and 3-4x inference speedups.
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Voltron: Enabling Elastic Multi-Device Execution of LLM Inference for Empowered Edge Intelligence
A framework called Voltron elastically distributes LLM inference across heterogeneous edge devices using layer-wise hybrid parallelism and mixed precision, achieving up to 16.5% higher accuracy than single-device execution while meeting QoS latency constraints.
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A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability
RRFP introduces a readiness-driven runtime for pipeline parallelism that uses schedules as hints and ready-set arbitration to improve utilization under runtime variability, reporting up to 2.77x speedup on multimodal workloads.
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Distilling step-by-step uses LLM-generated rationales as additional supervision in a multi-task framework so that 770M-parameter models outperform 540B-parameter models on NLP benchmarks with only 80% of the data.
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Hybrid JIT-CUDA Graph Optimization for Low-Latency Large Language Model Inference
A hybrid JIT-CUDA Graph framework reduces TTFT by up to 66% and P99 latency versus TensorRT-LLM for single-GPU LLaMA-2 7B inference on short prompts.