Posts by Diptorup Deb

FlashInfer on ROCm: High‑Throughput Prefill Attention via AITER

The explosive growth of large language models (LLMs) like DeepSeek-R1, Llama 3, and Qwen 3 has created an urgent need for efficient inference solutions. As these models scale to billions of parameters and context lengths extend to hundreds of thousands of tokens, the attention mechanism becomes a critical bottleneck, consuming substantial memory for key-value (KV) caches and requiring significant compute for each token generated.

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Enabling FlashInfer on ROCm for Accelerated LLM Serving

FlashInfer is an innovative framework designed to accelerate inference of large language models (LLMs). Given the explosive growth and adoption of models like DeepSeek R1, Llama 3, and Qwen 3, efficient inference is critical to meet the demands of real-world deployment. However, challenges such as GPU memory bottlenecks, throughput limitations, and latency remain significant hurdles for deploying these models at scale.

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