Cover Image

Forecasting LLM Inference Performance via Hardware-Agnostic Analytical Modeling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Patwari, Rajeev, Sirasao, Ashish, Das, Devleena
Format:	Preprint
Published:	2025
Subjects:	Performance Artificial Intelligence Hardware Architecture Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Recover-LoRA: Data-Free Accuracy Recovery of Degraded Language Models via Low-Rank Adaptation
by: Das, Devleena, et al.
Published: (2025)

Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management
by: Zhang, Hang, et al.
Published: (2025)

Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
by: Lübeck, Konstantin, et al.
Published: (2024)

It's all about PR -- Smart Benchmarking AI Accelerators using Performance Representatives
by: Jung, Alexander Louis-Ferdinand, et al.
Published: (2024)

Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference
by: Javat, Abdurrahman, et al.
Published: (2026)

RooflineBench: A Benchmarking Framework for On-Device LLMs via Roofline Analysis
by: Bi, Zhen, et al.
Published: (2026)

Accelerating LLM Inference via Dynamic KV Cache Placement in Heterogeneous Memory System
by: Fang, Yunhua, et al.
Published: (2025)

Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion
by: Nasr-Esfahany, Arash, et al.
Published: (2025)

NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
by: Yang, Hanchen, et al.
Published: (2025)

SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered CPUs
by: AbouElhamayed, Ahmed F., et al.
Published: (2025)

FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing
by: Müller, Mika Markus, et al.
Published: (2025)

Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
by: Chhugani, Jatin, et al.
Published: (2026)

Characterizing and Understanding HGNN Training on GPUs
by: Han, Dengke, et al.
Published: (2024)

Strassen Multisystolic Array Hardware Architectures
by: Pogue, Trevor E., et al.
Published: (2025)

Karatsuba Matrix Multiplication and its Efficient Custom Hardware Implementations
by: Pogue, Trevor E., et al.
Published: (2025)

Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey
by: Du, Dayou, et al.
Published: (2024)

Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
by: Zhang, Tim, et al.
Published: (2022)

Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
by: Chrapek, Marcin, et al.
Published: (2025)

Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective
by: Shi, Tianyao, et al.
Published: (2025)

MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
by: Zhang, Yu, et al.
Published: (2024)

Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers
by: Renney, Harri, et al.
Published: (2026)

AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design
by: Liang, Yanbiao, et al.
Published: (2025)

LLM-Driven Design Space Exploration of FPGA-based Accelerators
by: Sharma, Vinamra, et al.
Published: (2026)

The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution
by: Panigrahy, Deepak, et al.
Published: (2026)

Data-Driven Power Modeling and Monitoring via Hardware Performance Counter Tracking
by: Mazzola, Sergio, et al.
Published: (2025)

Challenges and Research Directions for Large Language Model Inference Hardware
by: Ma, Xiaoyu, et al.
Published: (2026)

ZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
by: Fan, Ruibo, et al.
Published: (2026)

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
by: Liao, Gang, et al.
Published: (2025)

Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads
by: Karami, Rachid, et al.
Published: (2024)

USEFUSE: Uniform Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks
by: Ibrahim, Muhammad Sohail, et al.
Published: (2024)

SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
by: Zhang, Jintao, et al.
Published: (2025)

Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML
by: John, Chelsea Maria, et al.
Published: (2024)

GCL-Sampler: Discovering Kernel Similarity for Sampled GPU Simulation via Graph Contrastive Learning
by: Wang, Jiaqi, et al.
Published: (2026)

Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities
by: Cavagna, Hiari Pizzini, et al.
Published: (2025)

HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation
by: Chen, Yao, et al.
Published: (2022)

KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer
by: Al-Qawlaq, Aness, et al.
Published: (2024)

DRAGON (Differentiable Graph Execution) : A suite of Hardware Simulation and Optimization tools for Modern AI/Non-AI Workloads
by: Sethi, Khushal
Published: (2022)

PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
by: Zhang, Kaixuan, et al.
Published: (2026)

SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Accelerators
by: Odema, Mohanad, et al.
Published: (2024)

Prefill vs. Decode Bottlenecks: SRAM-Frequency Tradeoffs and the Memory-Bandwidth Ceiling
by: Atmer, Hannah, et al.
Published: (2025)