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Karthik Ganesan

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#machine learning Preprint Oct 2026

Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs

Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. However, the extensive history of NVIDIA-specific optimizations hinders performance portability to other hardware. This paper presents our methods for optimizing alternativ...

Hao-Yu Zhang, Arshad Rahman, Adnan Aziz et al. · 0 citations

Characterizing self-speculative decoding approaches for accelerating LLMs

Five self-speculative decoding techniques are characterized across three model sizes and three datasets and recommendations for future research in this area are provided.

Jungmin Ha, Karthik Ganesan, Anh Nguyen et al. · 0 citations

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