Dhruv Rawat
@imdhruv.bsky.social
distributed systems, networks and ml
prev: yugabyte, cs@bitspilani
prev: yugabyte, cs@bitspilani
David Patterson & Xiaoyu Ma (Google) have written a new paper in which they argue why LLM inference is not just about compute but has its own distinct hardware challenges, while proposing four promising research directions.
arxiv.org/abs/2601.05047
arxiv.org/abs/2601.05047
Challenges and Research Directions for Large Language Model Inference Hardware
Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI tr...
arxiv.org
January 25, 2026 at 9:01 AM
David Patterson & Xiaoyu Ma (Google) have written a new paper in which they argue why LLM inference is not just about compute but has its own distinct hardware challenges, while proposing four promising research directions.
arxiv.org/abs/2601.05047
arxiv.org/abs/2601.05047