24 citations · 110 across the 20 of their papers we have counts for
24 papers
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression
Sasho Nedelkoski, Alexander Acker, Soeren Becker +2
Compressing the key-value (KV) cache is essential for serving long-context large language models (LLMs) under memory constraints. While current scalar quantization methods achieve…
Reconciling Bottom-Up Metrics with Top-Down Reporting for Cloud Carbon Accounting
Philipp Wiesner, Loïc Lannelongue, Alexander Acker +1
Organizations seeking to reduce the carbon footprint of their cloud applications must rely on two largely disconnected ways of measuring it, each with important limitations. Bottom…
Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation
Taehoon Kim, Seunggeun Cho, Dongsu Han
Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill reas…
Beyond Microservices: Testing Web-Scale RCA Methods on GPU-Driven LLM Workloads
Dominik Scheinert, Alexander Acker, Thorsten Wittkopp +6
Large language model (LLM) services have become an integral part of search, assistance, and decision-making applications. However, unlike traditional web or microservices, the hard…
Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study
Philipp Wiesner, Soeren Becker, Brett Cornick +3
Training large language models (LLMs) requires substantial compute and energy. At the same time, renewable energy sources regularly produce more electricity than the grid can absor…
What happens when nanochat meets DiLoCo?
Alexander Acker, Soeren Becker, Sasho Nedelkoski +3
Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distribute…