# Context Parallelism for Scalable Million-Token Inference

arXiv: https://arxiv.org/abs/2411.01783 · HTML: https://mlsanity.com/paper/2411.01783/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 52 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Fast Transformer Decoding: One Write-Head is All You Need](https://mlsanity.com/paper/1911.02150/) (2019)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving](https://mlsanity.com/paper/2401.09670/) (2024)
- [KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization](https://mlsanity.com/paper/2401.18079/) (2024)
- [Efficiently Scaling Transformer Inference](https://mlsanity.com/paper/2211.05102/) (2022)
- [FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision](https://mlsanity.com/paper/2407.08608/) (2024)
- [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://mlsanity.com/paper/2310.01889/) (2023)
- [MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://mlsanity.com/paper/2407.02490/) (2024)
- [Reducing Activation Recomputation in Large Transformer Models](https://mlsanity.com/paper/2205.05198/) (2022)
- [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://mlsanity.com/paper/2309.14509/) (2023)
- [QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving](https://mlsanity.com/paper/2405.04532/) (2024)
- [Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention](https://mlsanity.com/paper/2404.07143/) (2024)
- [Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving](https://mlsanity.com/paper/2407.00079/) (2024)

## Built on by (most cited first, 26 of 26)

- [Rope to Nope and Back Again: A New Hybrid Attention Strategy](https://mlsanity.com/paper/2501.18795/) (2025)
- [Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction](https://mlsanity.com/paper/2604.08542/) (2026)
- [FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users](https://mlsanity.com/paper/2502.19312/) (2025)
- [Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training](https://mlsanity.com/paper/2411.13055/) (2024)
- [Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation](https://mlsanity.com/paper/2511.20714/) (2025)
- [Helix Parallelism: Rethinking Sharding Strategies for Interactive Multi-Million-Token LLM Decoding](https://mlsanity.com/paper/2507.07120/) (2025)
- [TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving](https://mlsanity.com/paper/2508.17219/) (2025)
- [CoDec: Prefix-Shared Decoding Kernel for LLMs](https://mlsanity.com/paper/2505.17694/) (2025)
- [Tensor-Parallelism with Partially Synchronized Activations](https://mlsanity.com/paper/2506.19645/) (2025)
- [PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents](https://mlsanity.com/paper/2605.19932/) (2026)
- [Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models](https://mlsanity.com/paper/2602.03681/) (2026)
- [GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving](https://mlsanity.com/paper/2605.00831/) (2026)
- [Mesh-Attention: A New Communication-Efficient Distributed Attention with Improved Data Locality](https://mlsanity.com/paper/2512.20968/) (2025)
- [Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design](https://mlsanity.com/paper/2606.10493/) (2026)
- [Optimizing Long-context LLM Serving via Fine-grained Sequence Parallelism](https://mlsanity.com/paper/2511.06247/) (2025)
- [Design Space Exploration of DMA based Finer-Grain Compute Communication Overlap](https://mlsanity.com/paper/2512.10236/) (2025)
- [Parallelization Strategies for Dense LLM Deployment: Navigating Through Application-Specific Tradeoffs and Bottlenecks](https://mlsanity.com/paper/2603.05692/) (2026)
- [Stage-Aware Communication Scheduling for Disaggregated LLM Serving](https://mlsanity.com/paper/2603.17456/) (2026)
- [Move the Query, Not the Cache: Characterizing Cross-Instance Latent Attention Redistribution Across GPU Fabrics](https://mlsanity.com/paper/2606.01502/) (2026)
- [NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning](https://mlsanity.com/paper/2603.06798/) (2026)
- [The qs Inequality: Quantifying the Double Penalty of Mixture-of-Experts at Inference](https://mlsanity.com/paper/2603.08960/) (2026)
- [A Switch-Centric In-Network Architecture for Accelerating LLM Inference in Shared-Memory Network](https://mlsanity.com/paper/2603.28239/) (2026)
- [Accuracy Is Speed: Towards Long-Context-Aware Routing for Distributed LLM Serving](https://mlsanity.com/paper/2604.15732/) (2026)
- [NanoCP: Request-Level Dynamic Context Parallelism for Data-Expert Parallel Decoding](https://mlsanity.com/paper/2605.21100/) (2026)
- [KernelFlume: Elastic Core-Attention Scaling for Agentic Long-Context Decoding](https://mlsanity.com/paper/2606.29207/) (2026)
- [SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving](https://mlsanity.com/paper/2609.37626/) (2026)

## Abstract (arXiv)

We present context parallelism for long-context large language model inference, which achieves near-linear scaling for long-context prefill latency with up to 128 H100 GPUs across 16 nodes. Particularly, our method achieves 1M context prefill with Llama3 405B model in 77s (93% parallelization efficiency, 63% FLOPS utilization) and 128K context prefill in 3.8s. We develop two lossless exact ring attention variants: pass-KV and pass-Q to cover a wide range of use cases with the state-of-the-art performance: full prefill, persistent KV prefill and decode. Benchmarks on H100 GPU hosts inter-connected with RDMA and TCP both show similar scalability for long-context prefill, demonstrating that our method scales well using common commercial data center with medium-to-low inter-host bandwidth.