OmniPilot is a launch advisor that predicts serving costs for feasible configurations and abstains when requests fall outside its measured support envelope and ranks configurations using an economic utility metric calibrated to an operator's revealed preferences.
Abstract
Serving large language models (LLMs) on a shared, heterogeneous GPU cluster requires users and operators to select the GPU type, tensor-parallel degree, and precision before committing valuable node-hours. Making these choices is challenging because effective throughput, launch-success rates, and cluster demand and utilization continuously fluctuate. Furthermore, static configuration recipes miss critical interactions: quantization effects depend heavily on the model family, key-value cache pressure creates size-by-precision trade-offs, and failure rates vary by more than twofold across different tensor-parallel degrees. Additionally, cluster resources are frequently constrained by unpredictable hardware failures. To address these challenges, we present \textbf{OmniPilot}, a launch advisor that predicts serving costs for feasible configurations and abstains when requests fall outside its measured support envelope. OmniPilot pairs a conformally calibrated quantile cost model (spanning eight serving targets) with an out-of-distribution (OOD) abstention layer. It ranks configurations using an economic utility metric calibrated to an operator's revealed preferences. In evaluations across 460 benchmark runs on A100, H100, and H200 hardware across four precisions, OmniPilot predicts aggregate throughput with a 6.2\% mean absolute percentage error (MAPE) and a log-space $R^2=0.92$. The advisor achieves 95\% top-1 accuracy with a mean utility regret of just 0.003. When tested on an OOD holdout of unsupported cells, prediction error climbs to 24-46\% and conformal intervals cover 0 of 5 points; however, the abstention layer successfully flags all five as low-confidence. Over time, these OOD scenarios will be integrated into the training dataset to continuously expand the advisor's support envelope.
LLMVisor is presented, a roofline-guided latency attribution model that captures the memory-bound and compute-bound phases via a concise piecewise-linear form over features proportional to FLOPs and memory I/O traffic and runs efficiently at microsecond scale.
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EPIC mitigates imbalance via performance-aware expert migration and runtime expert activation, and then improves communication with topology-adaptive transport kernels and fine-grained computation-communication overlap.
Jiamin Cao, Qingxu Li, Yaozhong Liu et al.· Proceedings of the ACM SIGCO...· 0 citations
Running large language models on consumer devices such as laptops and desktops is challenging because model weights often exceed GPU memory capacity, making offloading inference necessary to extend effective model capacity with CPU memory. Existing offloading systems, however, typically rely on coarse layer-level or expert-level scheduling, which overlooks substantial heterogeneity among tensors within the same layer and adapts poorly to changing hardware load conditions on such devices. This paper presents ATSInfer, a hybrid CPU-GPU inference system for consumer devices that performs offloading at tensor granularity. ATSInfer combines static tensor placement with load-aware dynamic transfer, and introduces asynchronous CPU-GPU coordination to efficiently schedule hardware storage, data movement, and computation across heterogeneous backends. We implement ATSInfer and evaluate it on representative consumer platforms using both dense and MoE models. Compared with existing systems, ATSInfer improves prefill throughput by up to 1.94$\times$ and decode throughput by up to 3.29$\times$, while also increasing GPU utilization and making more effective use of PCIe bandwidth. These results show that ATSInfer can substantially improve the user experience of local LLM deployment on personal consumer devices.
Yangyijian Liu, Hongyi Ye, Mingyang Li et al.· 0 citations
GPU energy consumption represents a primary operational cost for Large Language Model (LLM) inference services. Despite the inherent variability of production workloads, most deployments rely on static power configurations that fail to exploit the non-linear relationship between power limits and performance. Consequently, opportunities to reduce energy overhead while maintaining Service Level Agreements (SLAs) are often missed. While modern GPUs provide hardware-level powercapping interfaces, their systematic application in a feedbackdriven manner to satisfy stringent latency guarantees remains significantly underexplored. This paper proposes a lightweight, non-intrusive adaptive GPU power-capping mechanism based on a PID controller that dynamically adjusts power limits using SLA feedback. The controller operates directly through vendor power-limit interfaces and incorporates guardrails such as deadbands, rate limiting, utilization-aware gating, and antiwindup protection. We evaluate the approach on a server with eight NVIDIA H200 GPUs running the qwen3:32b model under a time-varying Poisson workload, with SLA compliance defined over TTFT. Results show that static caps expose a rigid energy-SLA trade-off, whereas the adaptive controller achieves a 3.84% reduction in total energy consumption relative to a 700 W baseline while maintaining empirical SLA non-inferiority. These findings demonstrate that feedback-driven GPU power modulation can improve energy efficiency without compromising latency stability.
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MaverIQ is an intent-based LLM inference serving system that automatically maps user intents to deployment configurations while minimizing operational cost for the provider and reduces profiling overheads by 7-15× compared to state-of-the-art baselines.
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Despite fixed sampling parameters and random seeds, Large Language Model (LLM) inference exhibits output inconsistency, which undermines downstream tasks such as model evaluation and reinforcement learning. A major source of this nondeterminism is batch-dependent GPU execution: dynamic input shapes change kernel tiling and floating-point reduction orders. Existing systems address this problem with batch-invariant kernels, but these kernels restrict optimized tiling and split reductions, increasing more than 2$\times$ latency and reducing serving throughput by up to 74 %. This paper observes that although most kernels are not batch-invariant, they are position-invariant. Leveraging this property, we present CoRun, a scheduling-based system that achieves deterministic inference without requiring batch invariance. CoRun employs isolated prefill and fixed-shape batched decode to handle the two stages of LLM inference, respectively, leveraging CUDA graphs for efficient execution and simplified implementation. Experiments on LLMs with diverse architectures, including Qwen and DeepSeek, show that CoRun ensures determinism while improving throughput by 15-324 % over batch-invariant approaches, reducing time-to-first-token by 51.8 % and time-per-output-token by 48.6 % on average.
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