arXiv:2604.10180v2 Announce Type: replace-cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existing solutions operate at a coarse granularity and are tightly coupled to specific model architectures, leaving much room for performance improvement. This paper presents Tessera, the first kernel disaggregation system to improve performance and cost efficiency on heterogeneous GPUs for large model inference. Our key insight is that kernels within a single application exhibit diverse resource
The rapid increase in AI model complexity and the emergence of heterogeneous GPU clusters necessitate more efficient resource utilization.
This development could significantly improve the performance and cost-efficiency of large AI model inference, impacting companies reliant on extensive GPU compute.
The ability to disaggregate AI workloads at a kernel level allows for more granular and optimized use of diverse GPU hardware, moving beyond coarse-grained solutions.
- · Hyperscale cloud providers
- · AI model developers
- · GPU manufacturers with diverse product lines
- · Developers of legacy, tightly coupled AI solutions
- · Companies with homogenous, underutilized GPU infrastructure
Increased efficiency in AI inference on heterogeneous GPU clusters.
Reduced operational costs for AI services and broader accessibility of advanced AI models.
Accelerated development and deployment of more complex and diverse AI applications due to optimized hardware utilization.
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Read at arXiv cs.LG