Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
📰 ArXiv cs.AI
Learn how Coral efficiently serves multiple large language models over heterogeneous cloud GPUs, reducing costs and improving performance
Action Steps
- Deploy Coral on a cloud platform using heterogeneous GPUs
- Configure Coral to serve multiple LLMs concurrently
- Optimize model placement and resource allocation using Coral's adaptive algorithm
- Test and evaluate the performance of Coral with different LLMs and GPU configurations
- Compare the cost efficiency of Coral with traditional homogeneous GPU setups
Who Needs to Know This
Machine learning engineers and cloud architects can benefit from Coral's adaptive heterogeneity-aware approach to optimize LLM serving, while data scientists can utilize the cost-efficient solution to deploy multiple models
Key Insight
💡 Coral's adaptive heterogeneity-aware approach enables efficient serving of multiple LLMs over heterogeneous cloud GPUs, reducing costs and improving performance
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🚀 Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs 🚀
Key Takeaways
Learn how Coral efficiently serves multiple large language models over heterogeneous cloud GPUs, reducing costs and improving performance
Full Article
Title: Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
Abstract:
arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of mid-tier and older-generation GPUs that enjoy better availability and deliver comparable performance per dollar to top-tier hardware. To efficiently harness these heterogeneous resources for serving multiple LLMs concurrently, we introduce Coral, an adaptive heterogeneity-aware multi-LLM se
Abstract:
arXiv:2605.04357v1 Announce Type: cross Abstract: The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of mid-tier and older-generation GPUs that enjoy better availability and deliver comparable performance per dollar to top-tier hardware. To efficiently harness these heterogeneous resources for serving multiple LLMs concurrently, we introduce Coral, an adaptive heterogeneity-aware multi-LLM se
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