MIST is a new simulator for heterogeneous multi-stage LLM inference that combines hardware traces with analytical models to explore configuration trade-offs in hybrid CPU-accelerator systems.
Vidur: A large-scale simulation framework for llm inference
7 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Optimizing the deployment of Large language models (LLMs) is expensive today since it requires experimentally running an application workload against an LLM implementation while exploring large configuration space formed by system knobs such as parallelization strategies, batching techniques, and scheduling policies. To address this challenge, we present Vidur - a large-scale, high-fidelity, easily-extensible simulation framework for LLM inference performance. Vidur models the performance of LLM operators using a combination of experimental profiling and predictive modeling, and evaluates the end-to-end inference performance for different workloads by estimating several metrics of interest such as latency and throughput. We validate the fidelity of Vidur on several LLMs and show that it estimates inference latency with less than 9% error across the range. Further, we present Vidur-Search, a configuration search tool that helps optimize LLM deployment. Vidur-Search uses Vidur to automatically identify the most cost-effective deployment configuration that meets application performance constraints. For example, Vidur-Search finds the best deployment configuration for LLaMA2-70B in one hour on a CPU machine, in contrast to a deployment-based exploration which would require 42K GPU hours - costing ~218K dollars. Source code for Vidur is available at https://github.com/microsoft/vidur.
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LLM serving should triage by five-resource analytical floors and wall ordering, not grid search; on 16×H20, TP16 is capacity-capped at ~70 while EP+DP attention reaches ~644 concurrent 8K requests.
RouteBalance fuses routing and load balancing for heterogeneous LLM serving and traces the upper quality-cost-throughput frontier on a 13-instance 28-GPU cluster.
Dooly reduces LLM inference profiling GPU-hours by 56.4% across 12 models while keeping simulation MAPE under 5% for TTFT and 8% for TPOT by making profiling configuration-agnostic and redundancy-aware.
PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
Operator-level attention-FFN disaggregation enables ~4k tokens/s throughput for DeepSeek-V3.2 under tight TTFT/TPOT SLOs where chunked-prefill and prefill-decode baselines cannot.
Charon is a unified modular simulator that predicts LLM training and inference performance with under 5.35% error and identifies throughput improvements over baselines in a real deployment case.
citing papers explorer
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MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
MIST is a new simulator for heterogeneous multi-stage LLM inference that combines hardware traces with analytical models to explore configuration trade-offs in hybrid CPU-accelerator systems.
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Think Before You Grid-Search: Floor-First Triage for LLM Serving
LLM serving should triage by five-resource analytical floors and wall ordering, not grid search; on 16×H20, TP16 is capacity-capped at ~70 while EP+DP attention reaches ~644 concurrent 8K requests.
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RouteBalance: Fused Model Routing and Load Balancing for Heterogeneous LLM Serving
RouteBalance fuses routing and load balancing for heterogeneous LLM serving and traces the upper quality-cost-throughput frontier on a 13-instance 28-GPU cluster.
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Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation
Dooly reduces LLM inference profiling GPU-hours by 56.4% across 12 models while keeping simulation MAPE under 5% for TTFT and 8% for TPOT by making profiling configuration-agnostic and redundancy-aware.
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PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
PipeWeave predicts GPU kernel performance with 6.1% average error and end-to-end inference with 8.5% error by feeding analytical pipeline features into ML, cutting prior method errors by 4-7x across 11 GPUs.
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How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
Operator-level attention-FFN disaggregation enables ~4k tokens/s throughput for DeepSeek-V3.2 under tight TTFT/TPOT SLOs where chunked-prefill and prefill-decode baselines cannot.
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Charon: A Unified and Fine-Grained Simulator for Large-Scale LLM Training and Inference
Charon is a unified modular simulator that predicts LLM training and inference performance with under 5.35% error and identifies throughput improvements over baselines in a real deployment case.