[Paper] Data Driven Optimization of GPU efficiency for Distributed LLM Adapter Serving

Source: arXiv - 2602.24044v1

Overview

Large Language Model (LLM) adapters let developers specialize massive models without the cost of fine‑tuning each one, but serving hundreds of adapters simultaneously on a GPU cluster is a logistical nightmare. This paper introduces a data‑driven pipeline that automatically decides where each adapter should run so that a target workload can be handled with the fewest possible GPUs, while still meeting latency and memory constraints.

Key Contributions

• Digital Twin (DT) for LLM‑adapter serving – a high‑fidelity simulator that reproduces real‑world throughput and memory usage with < 5 % error, yet runs up to 90× faster than full benchmarking.
• Distilled ML performance model – a lightweight predictor trained on DT‑generated data that can estimate per‑GPU throughput in milliseconds, enabling rapid “what‑if” analyses.
• Greedy placement algorithm – leverages the ML predictor to allocate adapters to GPUs, maximizing throughput and minimizing the number of GPUs needed without causing request starvation.
• Comprehensive evaluation – shows up to a 30‑40 % reduction in required GPUs for realistic workloads, and demonstrates that the same pipeline can be repurposed for latency‑focused objectives.

Methodology

1. Workload Characterization – the system first collects statistics (request rates, adapter sizes, token lengths, etc.) from the production serving environment.
2. Digital Twin Construction – using these stats, the authors built a simulator that mimics the GPU’s execution pipeline (kernel launches, memory allocation, cache behavior). The DT runs many “virtual” experiments quickly, producing a large dataset of adapter‑GPU throughput pairs.
3. Model Distillation – a compact regression model (e.g., Gradient Boosted Trees) is trained on the DT data to predict the maximum sustainable throughput for any adapter‑GPU combination. Because the model is tiny, inference is virtually instantaneous.
4. Greedy Placement – starting with the most demanding adapters, the algorithm assigns each to the GPU that, according to the ML predictor, can still meet its throughput target. If no GPU can accommodate the adapter, a new GPU is provisioned. The process repeats until all adapters are placed.

The pipeline is fully automated: once the workload profile is fed in, the DT runs offline, the ML model is trained, and the placement decision is output in seconds.

Results & Findings

Key takeaways: the DT’s throughput estimates are accurate enough to drive placement decisions, and the distilled ML model makes the optimization loop fast enough for near‑real‑time re‑planning when workloads shift.

Practical Implications

• Cost Savings – Cloud providers and enterprises can cut GPU spend by up to a third for large‑scale adapter serving clusters.
• Scalable Ops – The fast “what‑if” capability lets SRE teams re‑balance adapters on the fly as traffic patterns change, avoiding costly over‑provisioning.
• Simplified Deployment – Developers no longer need deep expertise in GPU memory budgeting; the pipeline handles cache and memory constraints automatically.
• Extensible Objectives – By swapping the objective function (e.g., minimizing tail latency instead of GPU count), the same framework can be used for latency‑critical services such as real‑time code generation or chat assistants.
• Portability – Although demonstrated on NVIDIA GPUs, the DT abstraction can be adapted to other accelerators (AMD, Habana) with modest effort, making the approach vendor‑agnostic.

Limitations & Future Work

• Static Workload Assumption – The current pipeline optimizes for a snapshot of request rates; rapid, unpredictable spikes may still cause temporary overloads.
• Greedy Heuristic – While effective, the placement algorithm is not provably optimal; exploring more sophisticated combinatorial solvers could squeeze out additional efficiency.
• Adapter Diversity – The study focuses on adapters that share the same base LLM; extending to heterogeneous base models (e.g., mixing GPT‑3‑like and BERT‑style models) remains an open challenge.
• Hardware Variability – The DT was calibrated on a specific GPU generation; re‑calibration is required for newer architectures, though the authors note the process is automated.

Overall, the paper offers a pragmatic, data‑driven toolkit that bridges the gap between academic performance modeling and the day‑to‑day operational concerns of developers running large‑scale LLM services.

Authors

• Ferran Agullo
• Joan Oliveras
• Chen Wang
• Alberto Gutierrez-Torre
• Olivier Tardieu
• Alaa Youssef
• Jordi Torres
• Josep Ll. Berral

Paper Information

• arXiv ID: 2602.24044v1
• Categories: cs.DC, cs.AI, cs.CL, cs.LG
• Published: February 27, 2026
• PDF: Download PDF