Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
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 demands, making them the most suitable granularity for aligning computation with hardware capabilities. Tessera integrates offline analysis with online ad...
| Authors | Tiancheng Hu, Jin Qin, Zheng Wang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 11 April 2026 |
| Last updated at source | 19 September 2026 |
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