Data Driven Optimization of GPU efficiency for Distributed LLM Adapter Serving
| Authors | Ferran Agullo et al. |
| Year | 2026 |
| Field | AI / ML |
| arXiv | 2602.24044 |
| Download | |
| Categories | cs.DC, cs.AI, cs.CL, cs.LG |
Abstract
Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of adapters must be hosted concurrently. While prior work has largely focused on latency minimization, resource efficiency through throughput maximization remains underexplored. This paper presents a data-driven pipeline that, for a given workload, computes an adapter placement that serves the workload with the minimum number of GPUs while avoiding request starvation and GPU memory errors. To that end, the approach identifies the maximum feasible throughput attainable on each GPU by leveraging accurate performance predictions learned from real serving behavior. The proposed pipeline integrates three components: (i) a Digital Twin (DT) tailored to LLM-adapter serving, (ii) a distilled machine learning (ML) model trained on DT-generated data, and (iii) a greedy placement algorithm that exploits ML-based performance estimates to maximize GPU efficiency. The DT emulates real system dynamics with high fidelity, achieving below 5% throughput estimation error while executing up to 90 times faster than full LLM benchmarking across both predictable and unpredictable workloads. The learned ML models further accelerate performance estimation with marginal accuracy degradation, enabling scalable optimization. Experimental results demonstrate that the pipeline substantially improves GPU efficiency by reducing the number of GPUs required to sustain target workloads. Beyond GPU efficiency, the pipeline can be adapted to alternative objectives, such as latency minimization, highlighting its versatility for future large-scale LLM serving infrastructures.
Engineering Breakdown
Plain English
This paper tackles the problem of efficiently serving hundreds of LLM adapters simultaneously on distributed GPU clusters. Rather than focusing on latency (which prior work emphasizes), the authors optimize for throughput and GPU resource utilization — specifically, they compute adapter placements that minimize the total number of GPUs needed while avoiding request starvation and out-of-memory errors. Their data-driven pipeline learns accurate performance predictions from real serving workloads and uses this to identify maximum feasible throughput per GPU, then solves an optimization problem to place adapters across the cluster. The core insight is that maximizing resource efficiency requires understanding actual GPU behavior under load, not just theoretical capacity.
Core Technical Contribution
The key innovation is a data-driven performance prediction and optimization pipeline specifically designed for multi-adapter LLM serving that explicitly targets throughput maximization and resource efficiency rather than latency reduction. Rather than treating adapter placement as a static or heuristic problem, the authors build a learned performance model that captures how different adapter combinations, batch sizes, and configurations actually behave on real hardware, then feed this into a global optimization layer. This differs from prior work because it: (1) uses measured serving telemetry rather than model size or parameter counts to guide placement decisions, (2) explicitly models the throughput-memory trade-off that emerges in multi-adapter scenarios, and (3) formulates placement as a data-informed optimization problem that avoids starvation while minimizing GPU count. The insight that accurate performance prediction is prerequisite to efficient scheduling is novel in the adapter-serving domain.
How It Works
The pipeline ingests a workload specification (adapter access patterns, request rates, batch characteristics) and real GPU serving traces. First, a performance prediction component (likely a regression or surrogate model) learns the relationship between adapter configurations and actual throughput/latency/memory utilization from historical data — this captures non-linear interactions like how adding another adapter in GPU memory affects batching efficiency. Second, the system enumerates feasible adapter placements on the available GPU cluster, filtering out placements that violate memory constraints or would cause request starvation (adapters starved of GPU time). Third, for each feasible placement, the performance model predicts achievable throughput across the cluster. Finally, an optimization algorithm (likely integer linear programming or similar) selects the placement that maximizes throughput while minimizing GPU count. The output is a concrete assignment of which adapters live on which GPUs, along with scheduling parameters.
Production Impact
For engineers running LLM serving infrastructure, this directly cuts operational costs by reducing the number of GPUs needed to serve a given workload — if your company runs 500 adapters and today uses 100 GPUs, this approach might prove you only need 70. The data-driven nature means you can continuously retrain the performance model as workloads shift, hardware updates, or new adapter patterns emerge, keeping placement decisions aligned with reality rather than static heuristics. Integration requires: (1) logging detailed serving telemetry (throughput, latency, memory per batch/adapter), (2) building a regression model on that data (moderate ML engineering complexity), and (3) running a batch optimization job to recompute placements (can happen hourly or daily). The main trade-off is accuracy of the performance model — if your production hardware differs from training hardware, or if workloads have high variance, predictions may degrade, requiring careful model validation and fallback heuristics.
Limitations and When Not to Use This
The paper assumes serving workloads are static or change slowly enough that periodic recomputation is feasible — highly dynamic, burst-driven workloads may exceed the placement's capacity without warning. It also assumes the learned performance model generalizes to the actual production environment; if your GPUs, CUDA versions, or adapter implementations differ from training data, predictions could be significantly off. The approach likely requires substantial historical data to train accurate models, which is unavailable for newly launched adapters or unusual workloads. Additionally, the paper focuses on placement optimization but does not address dynamic scheduling during runtime (what happens when an adapter unexpectedly becomes hot?), request admission control (when to reject requests), or how to handle failures and migrations. Finally, the interaction between multiple adapters in shared GPU memory can be highly complex and hardware-specific, so the performance model may not capture second-order effects like L2 cache contention or scheduler jitter.
Research Context
This work builds on the growing literature around efficient LLM serving (e.g., vLLM, PagedAttention) and adapter-based model customization (LoRA, prefix tuning), but shifts focus from latency-optimal scheduling to resource-efficient placement. It joins recent systems papers (e.g., Ansor, MetaScheduler) that use learned performance models to guide infrastructure decisions, applying that paradigm to a new domain. The work likely improves upon baseline placement strategies (random, greedy, or size-based heuristics) on standard benchmarks (synthetic workloads or traces from cloud providers), and opens questions about how to extend this to heterogeneous hardware clusters, adaptive scheduling, or cross-model interactions. The broader research direction it enables is treating LLM serving infrastructure as a learned optimization problem, where data from production systems directly drives hardware allocation decisions.
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