HybridGen achieves 1.41x-3.2x average speedups over six prior KV cache methods for LLM inference by using attention logit parallelism, a feedback-driven scheduler, and semantic-aware KV cache mapping.
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SSV presents a sparse speculative-verification framework that resolves mismatches between speculative decoding and dynamic sparse attention to deliver up to 3.49x end-to-end throughput and 6.86x kernel speedups on NVIDIA H100 GPUs.
PLENA introduces a co-designed system with three optimization pathways for long-context agentic LLM inference, claiming up to 2.23x throughput over A100 and 4.04x energy efficiency.
citing papers explorer
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HybridGen: Efficient LLM Generative Inference via CPU-GPU Hybrid Computing
HybridGen achieves 1.41x-3.2x average speedups over six prior KV cache methods for LLM inference by using attention logit parallelism, a feedback-driven scheduler, and semantic-aware KV cache mapping.
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SSV: Sparse Speculative Verification for Efficient LLM Inference
SSV presents a sparse speculative-verification framework that resolves mismatches between speculative decoding and dynamic sparse attention to deliver up to 3.49x end-to-end throughput and 6.86x kernel speedups on NVIDIA H100 GPUs.
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Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
PLENA introduces a co-designed system with three optimization pathways for long-context agentic LLM inference, claiming up to 2.23x throughput over A100 and 4.04x energy efficiency.