mlsanityData to 14 Oct 2026

Frontier: Simulating the Next Generation of LLM Inference Systems

arXiv 2508.03148 ↗First posted 5 Aug 2025cs.LG cs.AI cs.DCcited by 2

On the map: Mixture-of-experts → Efficient MoE training and inference systems

also in 1 more
Abstract (arXiv)

Large Language Model (LLM) inference is growing increasingly complex with the rise of Mixture-of-Experts (MoE) models and disaggregated architectures that decouple components like prefill/decode (PD) or attention/FFN (AF) for heterogeneous scaling. Existing simulators, architected for co-located, dense models, are unable to capture the intricate system dynamics of these emerging paradigms. We present Frontier, a high-fidelity simulator designed from the ground up for this new landscape. Frontier introduces a unified framework to model both co-located and disaggregated systems, providing native support for MoE inference with expert parallelism (EP). It enables the simulation of complex workflows like cross-cluster expert routing and advanced pipelining strategies for latency hiding. To ensure fidelity and usability, Frontier incorporates refined operator models for improved accuracy. Frontier empowers the community to design and optimize the future of LLM inference at scale.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Efficient MoE training and inference systems, one of 12 directions in Mixture-of-experts. Builds on 12 of its 14 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

12 of 14 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism3 Apr
  2. 2023A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training11 Mar
  3. 2022Accelerating Distributed MoE Training and Inference with Lina31 Oct

Other references · 9

  1. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2024Qwen2 Technical Reportcited by 2,120
  4. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,141
  5. 2024DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Servingcited by 617
  6. 2024Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Servecited by 526
  7. 2024Vidur: A Large-Scale Simulation Framework For LLM Inferencecited by 117
  8. 2024LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scalecited by 31
  9. 2025Beyond the Buzz: A Pragmatic Take on Inference Disaggregationcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Samplingcited by 0
  2. 2026Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Faster MoE LLM Inference for Extremely Large Models
  2. 2024Performance Characterization of Expert Router for Scalable LLM Inference
  3. 2026A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference
  4. 2023EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
  5. 2025Taming the Titans: A Survey of Efficient LLM Inference Serving
  6. 2024LLM Inference Unveiled: Survey and Roofline Model Insights
  7. 2024GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments
  8. 2026Parallelization Strategies for Dense LLM Deployment: Navigating Through Application-Specific Tradeoffs and Bottlenecks