BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching
arXiv:2411.16102v2 Announce Type: replace Abstract: Offline batch inference, which leverages the flexibility of request batching to achieve higher throughput and lower costs, is becoming more popular for latency-insensitive applications. Meanwhile, recent progress in model capability and modality makes requests more diverse in compute and memory demands, creating unique opportunities for throughput improvement by resource overlapping. However, a request schedule that maximizes resource overlapping can conflict with the schedule that maximizes prefix sharing, a widely-used performance optimizat
The proliferation of increasingly large and diverse AI models necessitates more efficient inference methods to manage computational costs and throughput. This paper addresses current optimization challenges in offline AI inference.
Optimizing offline inference for large auto-regressive models directly impacts the cost-effectiveness, scalability, and accessibility of advanced AI applications. Better resource utilization enables wider adoption and more sophisticated use cases.
This research introduces methods to overcome conflicts between maximizing prefix sharing and resource overlapping in offline batch inference, potentially leading to significant improvements in throughput and cost efficiency for AI service providers.
- · Cloud AI service providers
- · Companies using large auto-regressive models
- · AI infrastructure developers
- · Inefficient AI inference architectures
- · Companies with high AI operational costs
Reduced operational costs for AI inference will make sophisticated AI models more economically viable for a broader range of businesses.
This efficiency gain could accelerate the deployment of AI agents and other complex AI systems, as the computing bottleneck becomes less severe.
Increased accessibility to advanced AI capabilities might foster new innovations and applications across various sectors, democratizing access to powerful AI tools.
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