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

• URL: http://arxiv.org/abs/2311.04951v2
• Date: Tue, 9 Apr 2024 11:21:14 GMT
• Title: Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO
• Authors: Haim Barad, Ekaterina Aidova, Yury Gorbachev,
• Abstract summary: 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.
• Score: 0.6144680854063939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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