<ul><li>Accelerating large language model (LLM) inference for high throughput and low latency is crucial for real-world deployments.</li><li>Polar Sparsity is introduced as a new approach to handle sparsity in MLP and Attention layers to optimize inference at scale.</li><li>Selective GPU kernels for MLP and Attention computations offer up to 2.2x speedups without compromising accuracy across various batch sizes and sequence lengths.</li><li>This advancement showcases the scalability of contextual sparsity for large batch sizes, enabling substantial acceleration in LLM deployment systems.</li></ul>

Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity

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