Abstract: Processing-In-Memory (PIM) architectures alleviate the memory bottleneck in the decode phase of large language model (LLM) inference by performing operations like GEMV and Softmax in memory.
A GPU benchmarking toolkit for measuring Large Language Model (LLM) inference performance. This tool evaluates throughput, latency, and memory usage across different models, quantization levels, and ...
Abstract: On-device Large Language Model (LLM) inference enables private, personalized AI but faces memory constraints. Despite memory optimization efforts, scaling laws continue to increase model ...
At the start of 2025, I predicted the commoditization of large language models. As token prices collapsed and enterprises moved from experimentation to production, that prediction quickly became ...
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