This is my personal list of resources related to machine learning systems. Feel free to drop me an email if you think there’s something worth mentioning. I will try to update this page frequently to include the most recent stuffs in mlsys.

• Facebook’s external large-scale work
• NGC Container Doc: great for development, without having to manually install CUDA, pytorch, and other dependencies.
• Awesome-System-for-Machine-Learning: A curated list of research in machine learning systems (MLSys). Paper notes are also provided.
• Mastering LLM Techniques: Inference Optimization: summary of techniques used for LLM deployment
• LLM Visualization

Courses

• Deep Learning Systems: Algorithms and Implementation
• Machine Learning Compilation: offered by Tianqi Chen, intro to ML compiler. Open to all.
• 15-884: Machine Learning Systems: offered by Tianqi Chen at CMU.
• CSE 291F: Advanced Data Analytics and ML Systems: offered by Arun Kumar at UCSD.
• Tinyflow: tutorial code on how to build your own Deep Learning System in 2k Lines.
• CSE 599W: Systems for ML: offered by Tianqi Chen at UW.
• CS8803-SMR: Special Topics: Systems for Machine Learning: offered by Alexey Tumanov at Georgia Tech. [schedule]
• CS 744: Big Data Systems: offered by Aditya Akella back at UW-Madison.
• CS 329S: Machine Learning Systems Design: offered by Stanford.
• EECS 598: Systems for AI: offered by Mosharaf Chowdhury at UMich.
• CS 378: Systems for Machine Learning: offered by Aditya Akella at UT.
• Machine Learning Systems (Fall 2019): from UCB
• ECE 382V SysML: Computer Systems and Machine Learning Interplay: taught by Neeraja Yadwadkar at UT.
• CSE 234 Data Systems for Machine Learning from UCSD.
• CS336: Language Modeling from Scratch from Stanford.

Labs & Faculties

• CMU Catalyst
• Berkeley RISE Lab
• MIT DASIL Lab
• UW SAMPL
• SymbioticLab
• Shivaram Venkataraman at UW-Madison
• UTNS Lab
• Neeraja Yadwadkar at UT Austin
• SAIL: Systems for Artificial Intelligence Lab @ GT
• Amar Phanishayee @ MSR
• Hong Zhang @ University of Waterloo
• Manya Ghobadi @ MIT
• WukLab @ UCSD
• Hao AI Lab @ UCSD
• Junchen Jiang

Tutorials

• The Illustrated Transformer: best introduction for transformer
• Dive into Deep Learning: interactive deep learning book with code, math, and discussions.
• CS231n: Convolutional Neural Networks for Visual Recognition
• Pytorch-Internals: must-read for PyTorch basics.
• Differential Programming with JAX
• Getting started with JAX (MLPs, CNNs & RNNs)
• Physics-based Deep Learning
• 机器学习系统：设计和实现
• Dive into Deep Learning Compiler
• Autodidax: JAX core from scratch: really really good resource for learning Jax internals.
• Extending JAX with custom C++ and CUDA code
• Vector, Matrix, and Tensor Derivatives
• ML Memory Optimization: slides from UW. Visualization of dataflow graph helps understand how to optimize memory.
• Pytorch模型加速系列（一）——新的Torch-TensorRT以及TorchScript/FX/dynamo
• Semianalysis: many good posts
• How to Load PyTorch Models 340 Times Faster with Ray
• 分布式深度学习系统
• MLsys各方向综述
• 金雪锋: MindSpore 技术负责人
• 解读谷歌Pathways架构（一）：Single-controller与Multi-controller
• Why are ML Compilers so Hard?
• Alpa: Automated Model-Parallel Deep Learning
• Data Transfer Speed Comparison: Ray Plasma Store vs. S3
• 从零开始学深度学习编译器
• 一文搞懂 TorchDynamo 原理

LLM Optimization

• The Illustrated GRPO: Group Relative Policy Optimization Explained: very good tutorial showing how GRPO works with examples.

Communication

• PyTorch SymmetricMemory: Harnessing NVLink Programmability with Ease
• GPU-Initiated Networking for NCCL
• Triton-distributed: Programming Overlapping Kernels on Distributed AI Systems with the Triton Compiler

Seminars

• Stanford MLSys Seminar

Papers

This section could potentially be extremely long..

Training

Really broad topic…

• The Llama 3 Herd of Models: really great paper explaining how SOTA models are trained in real world!

LLM

You an also refer to Awesome-LLM

• Large Transformer Model Inference Optimization
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face: use LLM as the controller to coordinate with exernal models for complicated tasks
• Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity: load as sparse, compute as dense
• EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism: Overlapping LLM forward with KV caching computation.
• Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache: managing KV cache in distributed settings.
• MatFormer: Nested Transformer for Elastic Inference: adaptive blocks during inference.

NAS

• Puzzle: Distillation-Based NAS for Inference-Optimized LLMs: Applying block-wise local distillation to every alternative subblock replacement in parallel and scoring its quality and inference cost to build a “library” of blocks. Then, using Mixed-Integer-Programming to assemble a heterogeneous architecture that optimizes quality under constraints such as throughput, latency and memory usage.

Diffusion

• Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity
• EFFICIENT-VDIT: Efficient Video Diffusion Transformers with Attention Tile
• Timestep Embedding Tells: It’s Time to Cache for Video Diffusion Model

KV Cache

• CacheGen: KV Cache Compression and Streaming for Fast Language Model Serving
• Efficient Memory Management for Large Language Model Serving with PagedAttention: (vLLM) allows non-contiguous KV cache
• Efficiently Programming Large Language Models using SGLang: RadixAttention that allows Kv cache sharing in prompt engineering
• MODEL TELLS YOU WHAT TO DISCARD: ADAPTIVE KV CACHE COMPRESSION FOR LLMS: dynamic KV cached token dropping to long sequence.
• Efficient Streaming Language Models with Attention Sinks: (Stream-LLM)
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
• FlashDecoding++: Faster Large Language Model Inference on GPUs
• FlexAttention: The Flexibility of PyTorch with the Performance of FlashAttention
• vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

Datasets

• LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset: real-world conversation

ML Compilers

• TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
• Learning to Optimize Tensor Programs: facilitate efficient ML kernel search using ML.
• The OoO VLIW JIT Compiler for GPU Inference: JIT Compiler to enable better GPU multiplexing.
• LazyTensor: combining eager execution with domain-specific compilers: combining dynamic graph with JIT. summary
• Automatic Generation of High-Performance Quantized Machine Learning Kernels
• DietCode: Automatic Optimization for Dynamic Tensor Programs
• The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
• The Deep Learning Compiler: A Comprehensive Survey
• Graphiler: A Compiler for Graph Neural Networks: ML compiler specifically designed for GNN.

Graph Optimization

• Accelerating Multi-Model Inference by Merging DNNs of Different Weights: combine multiple instances of a model into one computational graph

Inference

• INFaaS: Automated Model-less Inference Serving: developers simply specify the performance and accuracy requirements for their applications without needing to specify a specific model-variant for each query
• DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale: transformer-specific inference optimization done by DeepSpeed.
• Clipper: A Low-Latency Online Prediction Serving System: a nice overview of inference system. Not SOTA but a good starter.
• Orloj: Predictably Serving Unpredictable DNNs: shares similarity to Clipper, but targeting models that may yield unpredictable performance.
• AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving: how to multiplex devices to serve multiple model while meeting latency constraint.
• MOSEL: Inference Serving Using Dynamic Modality Selection: dynamic modality selections for accuracy and SLO tradeoff
• BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models: finer-grained LLM serving
• Orca: A Distributed Serving System for Transformer-Based Generative Models: iteration-based LLM inference
• Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs: inference on heterogeneous accelerators using max-flow
• DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving: automatic configuration of inter and intra-node parallelism for model for both prefill and decoding.

Multitenancy

• MISO: exploiting multi-instance GPU capability on multi-tenant GPU clusters
• A Survey of Multi-Tenant Deep Learning Inference on GPU: efficient resource management for multi-tenant inference.
• Enable Simultaneous DNN Services Based on Deterministic Operator Overlap and Precise Latency Prediction: overlap DNN operators of different models in an online fashion
• THEMIS : Fair and Efficient GPU Cluster Scheduling: minimize the maximum finish time fairness across all ML apps while efficiently utilizing cluster GPUs

Dynamic Neural Network

• Dynamic Neural Networks: A Survey
• Dynamic Multimodal Fusion
• Hash Layers For Large Sparse Models: Using hashing for MoE gating.
• An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems: use evolution algorithm to update model structure during the training phase.
• FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping: dynamically skip FFN layers in Transformers suing cosine similarity.
• SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference: monotonically decrease LLM layers to ease KV cache management

Auto Placement

• Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning
• Beyond Data and Model Parallelism for Deep Neural Networks: FlexFlow.

Reasoning LLM

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Llama-Nemotron: Efficient Reasoning Models

Federated Learning

• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Switch & ML

• Do Switches Dream of Machine Learning? Toward In-Network Classification
• Taurus: A Data Plane Architecture for Per-Packet ML

Memory Management

• ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning

System Design

• Pathways: Asynchronous Distributed Dataflow for ML: Google’s new DL systems, specifically designed for TPU.
• Ray: A Distributed Framework for Emerging AI Applications: RiseLab’s new distributed system. Using shared memory for data communication.
• Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
• OneFlow: Redesign the Distributed Deep Learning Framework from Scratch: shared many similarities to Google’s Pathways.

Trade-off

• Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training: find trade-offs between DNN training performance optimization and energy consumption, by configuring batch size and GPU power limit.

Structured LLM Generation

• Efficient Guided Generation for Large Language Models: Outline
• Prompting Is Programming: A Query Language for Large Language Models

Async Training

• Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models: training individual LMs before merging
• Large Scale Distributed Deep Networks: parameter server

Self-play

• O1 Replication Journey: A strategic Progress Report

Costs

• RouteLLM: Learning to Route LLMs with Preference Data

RAG

• From Local to Global: A Graph RAG Approach to Query-Focused Summarization: GraphRAG, compressing information into graphs so retrieval may contain more information
• Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting: SpeculativeRAG: use smaller models with a subset of retrieved documents for faster draft generation, and use the larger LM for verification.
• Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models: summarize retrieval documents instead of merely just retrieving documents as is.
• Seven Failure Points When Engineering a Retrieval Augmented Generation System: a study showing how different configurations in different retrieval stages may affect generation quality.
• Active Retrieval Augmented Generation: determine when to perform retrieval, and how to induce the query for retrieval
• MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery