Efficient Inference for Large Language Models

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HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

HeteGen introduces a novel approach for heterogeneous parallel computing to reduce latency in Large Language Models (LLMs) on resource-constrained devices, achieving significant speed improvements. The core reasoning is to leverage CPU and I/O resources alongside GPUs to optimize computational efficiency and reduce the need for parameter transfers.

HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

HeteGen introduces a novel approach for heterogeneous parallel computing to reduce latency in Large Language Models on resource-constrained devices.

HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

HeteGen introduces a novel approach for efficient inference on resource-constrained devices by leveraging heterogeneous parallel computing, demonstrating significant improvements in speed.

Unified Layer Skipping: A Stable and Efficient Inference Strategy for Large Language Models

The proposed Unified Layer Skipping strategy determines the number of layers to skip based solely on the target speedup ratio, ensuring a stable and predictable acceleration effect across different input samples. Unlike existing methods that skip multiple contiguous layers, Unified Layer Skipping skips the corresponding number of intermediate layer computations in a balanced manner, minimizing the impact on the model's layer-wise representations.

Efficient Inference Techniques for Scaling Up Large Language Models

This survey presents a comprehensive taxonomy and analysis of techniques to enhance the efficiency of inference for large language models (LLMs), addressing challenges posed by their substantial computational and memory requirements.

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