A new technical paper titled “Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling” was published by researchers at Uppsala University. “Energy consumption ...
“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 ...
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Rearranging the computations and hardware used to serve large language ...
SHANGHAI--(BUSINESS WIRE)--VeriSilicon (688521.SH) today announced that its ultra-low energy and high-performance Neural Network Processing Unit (NPU) IP now supports on-device inference of large ...
NVIDIA Boosts LLM Inference Performance With New TensorRT-LLM Software Library Your email has been sent As companies like d-Matrix squeeze into the lucrative artificial intelligence market with ...
MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--Enfabrica Corporation, an industry leader in high-performance networking silicon for artificial intelligence (AI) and accelerated computing, today announced the ...
The company tackled inferencing the Llama-3.1 405B foundation model and just crushed it. And for the crowds at SC24 this week in Atlanta, the company also announced it is 700 times faster than ...
The history of computing teaches us that software always and necessarily lags hardware, and unfortunately that lag can stretch for many years when it comes to wringing the best performance out of iron ...
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