TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
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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.
KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.
Foundry uses template-based CUDA graph context materialization to reduce LLM serving cold-start latency by up to 99% while preserving CUDA graph throughput gains.
Amoeba adaptively adjusts tensor parallelism at runtime for LLM inference services to handle mixed short and long context requests, delivering 1.75x-6.57x throughput gains over prior solutions in real-world trace evaluations.
citing papers explorer
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TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
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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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KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.
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Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Start
Foundry uses template-based CUDA graph context materialization to reduce LLM serving cold-start latency by up to 99% while preserving CUDA graph throughput gains.
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Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services
Amoeba adaptively adjusts tensor parallelism at runtime for LLM inference services to handle mixed short and long context requests, delivering 1.75x-6.57x throughput gains over prior solutions in real-world trace evaluations.