HetRL delivers up to 9.17x higher throughput for LLM RL training on heterogeneous GPUs by using hybrid and ILP-based schedulers to solve a joint optimization problem over computation and data dependencies.
Thunderserve: High-performance and cost-efficient LLM serving in cloud environments
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HexiScale enables LLM training on heterogeneous GPUs via asymmetric parallelism and graph partitioning, matching homogeneous performance at equal FLOPS and delivering 1.5-2.4x higher throughput than prior heterogeneous systems.
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HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments
HetRL delivers up to 9.17x higher throughput for LLM RL training on heterogeneous GPUs by using hybrid and ILP-based schedulers to solve a joint optimization problem over computation and data dependencies.
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HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
HexiScale enables LLM training on heterogeneous GPUs via asymmetric parallelism and graph partitioning, matching homogeneous performance at equal FLOPS and delivering 1.5-2.4x higher throughput than prior heterogeneous systems.