The paper argues that AI deployment performance depends on interactions among compression, compiler transformations, and serving policies rather than just model architecture. It introduces a three‑layer taxonomy—model‑level techniques, compiler transformations, and system policies—and frames deployment as a constrained multi‑objective optimization problem over accuracy, latency, throughput, memory footprint, and energy. The authors propose an evidence protocol for comparable benchmarking and synthesize data from edge and data‑center platforms to show that cross‑layer interactions drive deployment outcomes, concluding with a constraint‑aware selection procedure and open research problems.
arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires bala...
The paper introduces a composite metric for quantizing small language models that balances information retention and throughput gains, using a normalized SQNR-based coefficient and roofline-based latency analysis. Profiling Gemma 3 1B shows that Feed‑Forward Network blocks and the embedding matrix are prime candidates for acceleration, with the metric enabling tuning of speed‑quality trade‑offs without actual execution. The authors demonstrate that their estimates predict accelerated speedup within about 4% error and allocate resources more effectively than evolutionary search or Shapley‑value methods.
arXiv:2606. 28565v1 Announce Type: cross Abstract: As large language models (LLMs) move into production serving, practitioners must rapidly evaluate inference performance across diverse hardware, models, and serving parameters to meet cost and latency targets.
arXiv:2608.28652v1 Announce Type: new Abstract: Artificial intelligence (AI) models have demonstrated remarkable capabilities across various domains, yet their widespread deployment is impeded by sig...
The paper introduces a composite metric for quantizing small language models that balances information retention and throughput gains. By profiling Gemma 3 1B, it identifies Feed‑Forward Network blocks and the embedding matrix as key acceleration targets, estimating quality and speed scores without actual execution. The metric, validated across architectures with ~4% prediction error, enables predictable, resource‑efficient quantization compared to evolutionary or Shapley‑based methods.
arXiv:2504. 20198v2 Announce Type: replace-cross Abstract: This work presents a comprehensive evaluation of neural network graph compilers across heterogeneous hardware platforms, addressing the critical gap between theoretical optimization techniques and practical deployment scenarios.
Edge LLM deployment is shaped by more than model size and precision: inference backend, hardware platform, memory traffic, and power management all affect latency and efficiency. We present Hydra, a c...
arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.
arXiv:2607. 20806v1 Announce Type: new Abstract: Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resource-constrained edge and mobile environments.
arXiv:2607. 14618v1 Announce Type: new Abstract: CPUs are the most universal target for on-device LLM inference, but existing low-bit quantization methods offer either coarse operating points or fine-grained mixed precision that is difficult to execute efficiently on CPUs.
arXiv:2608. 16336v1 Announce Type: cross Abstract: Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing.
The paper explores energy-aware knowledge distillation for large language models (LLMs) used in software engineering tasks such as clone detection, vulnerability prediction, and code summarization. It shows that the commonly used FLOPs metric does not reliably reflect actual energy consumption, and that using energy-surrogate models during distillation can reduce inference energy by up to 90% and memory usage by 86% with only modest accuracy loss. The study demonstrates that guiding distillation with direct energy estimates improves the sustainability and deployability of LLMs on consumer hardware.