#model-serving
#model-serving

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General Model Serving Systems and Memory Optimizations Explained | HackerNoon

Most model serving systems overlook the autoregressive nature of large language models, limiting their optimization potential.

PagedAttention and KV Cache Manager enhance memory efficiency and performance in LLM serving, especially for autoregressive tasks.

#machine-learning

Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Overall Results | HackerNoon

Apparate optimizes model serving by reducing latency significantly while meeting accuracy requirements, outperforming traditional methods across various workloads.

Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Additional Related Work | HackerNoon

Apparate offers a solution to latency-throughput tension in model serving with a focus on early-exit strategies.

Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Overall Results | HackerNoon

Apparate optimizes model serving by reducing latency significantly while meeting accuracy requirements, outperforming traditional methods across various workloads.

Apparate: Early-Exit Models for ML Latency and Throughput Optimization - Additional Related Work | HackerNoon

Apparate offers a solution to latency-throughput tension in model serving with a focus on early-exit strategies.

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