David Patterson: Challenges and Research Directions for LLM Inference Hardware
Large Language Model (LLM) inference faces significant challenges primarily related to memory and interconnect issues rather than compute power. The autoregressive Decode phase of Transformer models distinguishes LLM inference from training, complicating the process.
arxiv.org
2 min
1/25/2026
David Patterson: Challenges and Research Directions for LLM Inference Hardware
Large Language Model (LLM) inference faces significant challenges primarily related to memory and interconnect issues rather than compute power. The autoregressive Decode phase of Transformer models distinguishes LLM inference from training, complicating the process.
arxiv.org
2 min
1/25/2026
David Patterson: Challenges and Research Directions for LLM Inference Hardware
Large Language Model (LLM) inference faces significant challenges primarily related to memory and interconnect issues rather than compute power. The autoregressive Decode phase of Transformer models distinguishes LLM inference from training, complicating the process.
arxiv.org
2 min
1/25/2026
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