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The rapid rise of Large Language Models (LLMs) has prompted a re-evaluation of system architecture design, making energy efficiency and sustainability more crucial than ever. Recently, wafer-scale architectures have emerged as a viable alternative for LLM training and inference, as evidenced by the success of Cerebras Systems. In this work, we examine the carbon implications of wafer-scale architectures as compared to traditional GPUs. As a case study, we examine LLMs on a Cerebras CS-3 system in order to quantify power and total carbon. Then, we analyze total carbon delay product (tCDP) to evaluate the carbon efficiency and performance potential of these systems. We take the first step towards exploring this trade-off for wafer-scale versus traditional GPU architectures - and ultimately find there exists a rich design space, depending on workload and hardware configuration. 

Abstract Lyα emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrowband photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observedz = 2–4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ²value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.