arXiv Open Access 2023

Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO

Haim Barad Ekaterina Aidova Yury Gorbachev

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Abstrak

Inference optimizations are critical for improving user experience and reducing infrastructure costs and power consumption. In this article, we illustrate a form of dynamic execution known as speculative sampling to reduce the overall latency of text generation and compare it with standard autoregressive sampling. This can be used together with model-based optimizations (e.g. quantization) to provide an optimized solution. Both sampling methods make use of KV caching. A Jupyter notebook and some sample executions are provided.

Topik & Kata Kunci

cs.LG cs.AI cs.PF

Penulis (3)

Haim Barad

Ekaterina Aidova

Yury Gorbachev

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APA MLA BibTeX

Barad, H., Aidova, E., Gorbachev, Y. (2023). Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO. https://arxiv.org/abs/2311.04951

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓