Posts by Rishi Madduri
Enabling FlashInfer on ROCm for Accelerated LLM Serving
- 01 October 2025
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.
Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs
- 23 March 2025
Training massive deep-learning models requires a balance of efficiency and scalability. In the context of the Transformers architecture, Mixture of Experts (MoE) models are massive machine learning architectures characterized for dividing tasks among multiple specialized sub-networks or “experts”. A gating network determines the expert to which a given input should be routed, enabling the model to handle complex tasks more efficiently by using the specialized capabilities of each expert. This dynamic routing mechanism allows MoE models to scale efficiently, activating only a subset of the network for each input, therefore reducing computational load while maintaining high model capacity.