Disaggregated Speculative Decoding for Carbon-Efficient LLM Serving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Large language models (LLMs) are increasingly deployed in practice but incur significant computational costs and environmental impacts. Disaggregated serving techniques, particularly decoupling prefill and decoding (DPD) across GPUs, have been introduced to improve performance and reduce carbon emissions. However, DPD suffers from high bandwidth overhead due to frequent large KV cache transfers. To address this, we present disaggregated speculative decoding (DSD), which leverages speculative decoding by assigning draft models to older GPUs and target models to newer GPUs, requiring only token and probability distribution transfers. Building on this insight, we introduce GreenLLM, an SLO- and bandwidth- aware framework that unifies DPD and DSD, profiles workload characteristics, and dynamically selects the most carbon-efficient configuration. Across diverse benchmarks, GreenLLM reduces carbon emissions by up to 40.6% while meeting latency SLOs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it