Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning
📰 ArXiv cs.AI
Learn how Prism enables cost-efficient multi-LLM serving using GPU memory ballooning, improving resource efficiency for inference providers
Action Steps
- Analyze production traces to identify dynamic bursty-group patterns in LLM usage
- Apply GPU memory ballooning to allocate resources efficiently
- Implement Prism to adapt to variability in model activity
- Configure Prism to optimize resource allocation for multiple LLMs
- Test and evaluate the performance of Prism in a production environment
Who Needs to Know This
ML engineers and researchers can benefit from this technique to optimize LLM serving, while product managers can apply this to reduce costs and improve model availability
Key Insight
💡 Prism's GPU memory ballooning technique can efficiently serve multiple LLMs, adapting to dynamic usage patterns and reducing resource waste
Share This
🚀 Improve LLM serving efficiency with Prism, using GPU memory ballooning to reduce costs and increase model availability
Key Takeaways
Learn how Prism enables cost-efficient multi-LLM serving using GPU memory ballooning, improving resource efficiency for inference providers
Full Article
Title: Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning
Abstract:
arXiv:2505.04021v3 Announce Type: replace-cross Abstract: Inference providers must maintain availability for many LLMs, including low-volume but essential models, making resource efficiency increasingly important as token prices fall. Analysis of production traces reveals a dynamic bursty-group pattern in which sets of models become active together and shift over time; existing space- and time-sharing approaches lack principled mechanisms to adapt to this variability, forcing trade-offs between
Abstract:
arXiv:2505.04021v3 Announce Type: replace-cross Abstract: Inference providers must maintain availability for many LLMs, including low-volume but essential models, making resource efficiency increasingly important as token prices fall. Analysis of production traces reveals a dynamic bursty-group pattern in which sets of models become active together and shift over time; existing space- and time-sharing approaches lack principled mechanisms to adapt to this variability, forcing trade-offs between
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