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Background The paper discusses the deployment challenges of large language models (LLMs), focusing on the enormous resource demand in cloud services. Due to the enormous size of LLMs, efficient inference becomes critical.
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Existing Work High-efficiency inference relies mainly on quantization techniques, particularly integer quantization. However, even the most advanced 4-bit integer quantization, such as W4A4, often fails to perform as expected due to high runtime overhead for dequantizing weights or partial sums on GPUs.
- Introduced a new quantization algorithm named QoQ
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Challenge 1: High dequantization overhead Existing 4-bit quantization methods experience up to 20%-90% runtime overhead on GPUs during dequantization, severely affecting the throughput of LLM service. The QoQ algorithm effectively reduces dequantization latency through progressive group quantization and compute-aware weight reordering, providing more efficient processing.
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Challenge 2: Accuracy degradation Traditional 4-bit quantization methods limit their application due to accuracy loss. The QoQ algorithm introduces the SmoothAttention mechanism, which reduces accuracy degradation due to 4-bit KV quantization by altering the way activations are quantized.
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The QServe system implements the QoQ algorithm and achieves a throughput increase of 1.2-3.5 times on NVIDIA GPUs by using compute-aware weight reordering and register-level parallelism. The system design also considers memory binding and computational intensity adjustments, further enhancing performance.
With its novel quantization algorithm and system design, QServe significantly enhances the efficiency of LLM servicing on GPUs, dramatically reducing costs and providing a new solution for deploying large-scale language models.