arXiv Machine Learning
Jul 14
FastTPS: An Optimized Method for LLM Token Phase for AI accelerators
arXiv:2607. 11211v1 Announce Type: new Abstract: The popularity of large language models (LLMs) escalates an ongoing demand for effective inference.
Prefill/Decode-Aware Evaluation of LLM Inference on Emerging AI Accelerators
arXiv:2606. 17104v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in latency- and cost-sensitive settings, inference efficiency has become a central systems challenge.
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Hugging Face Trending Papers
Aug 25
Hydra: Phase-Aware Workload Characterization of LLM Inference across Edge SoC Generations, Backends, and Quantization Levels
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 AI
Jul 21
KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?
arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.
arXiv AI
Jun 2
Threshold-Based Exclusive Batching for LLM Inference
arXiv:2606. 00516v1 Announce Type: new Abstract: Mixed batching (MB)--interleaving prefill and decode in a single batch--has become the standard scheduling strategy for large language model (LLM) inference due to its efficiency in maximizing compute and memory utilization.
arXiv AI
Sep 23
RoofLang: Enabling AI-Driven Architecting of LLM Inference Systems
arXiv:2609.12551v2 Announce Type: replace-cross Abstract: AI is beginning to make substantive contributions to LLM inference optimization. Existing AI optimizations are predominantly profiling-based....
arXiv Machine Learning
Jul 8
Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators
arXiv:2607. 05400v1 Announce Type: cross Abstract: Generative AI models, such as Large Language Models (LLMs) and diffusion models, have demonstrated impressive performance across a wide range of tasks.
arXiv Machine Learning
Aug 31
Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms
arXiv:2608.28044v1 Announce Type: cross Abstract: Large language model (LLM) inference serving is priced by tokens, but GPU energy is consumed over inference windows. This accounting mismatch makes t...
arXiv Computation and Language
Sep 23
Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory-Efficient Diffusion LLMs
Flash-dLLM is a training‑free inference acceleration framework that improves the speed and memory efficiency of Diffusion Large Language Models (dLLMs). It tackles GPU memory I/O bottlenecks by introducing an I/O‑aware fused KV‑cache kernel and then employs a draft‑and‑verify decoding strategy that uses the dLLM itself as both drafter and verifier. Experiments on mathematical reasoning and code‑generation tasks show Flash‑dLLM outperforms existing acceleration methods, achieving up to 11.0× speedups over the Elastic‑Cache baseline.
arXiv AI
Jun 30
KernelSight-LM: A Kernel-Level LLM Inference Simulator
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 AI
Aug 11
LLMVisor: A Real-Time Latency Attribution Model for Multi-Tenant LLM Serving
arXiv:2608. 08382v1 Announce Type: new Abstract: As LLM inference shifts to multi-tenant GPU clusters, co-batching improves throughput but obscures per-tenant usage and limits control.
arXiv AI
Aug 25
LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization
arXiv:2608.21836v1 Announce Type: new Abstract: Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a...
arXiv Machine Learning
Jul 31
KernelGenBench: A Multi-Source and Multi-Chip Benchmark for LLM-based Kernel Generation
arXiv:2607. 27231v1 Announce Type: cross Abstract: Large language models (LLMs) have significantly increased the demand for efficient accelerator kernels, but kernel development remains a highly specialized and labor-intensive task.