# Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism

DevFeed: [Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism](<https://devfeed.tech/articles/scaling-llm-inference-innovations-in-tensor-parallelism-context-parallelism-and-expert-parallelism-30492.md>)

Original publisher: [Read original article](<https://engineering.fb.com/2025/10/17/ai-research/scaling-llm-inference-innovations-tensor-parallelism-context-parallelism-expert-parallelism/>)

Author: Cen Zhao; Xiaodong Wang; Jianyu Huang

Published: 2025-10-17T16:00:50Z

Content type: article

Language: en

Sources: [Meta AI Research](<https://devfeed.tech/sources/meta-ai-research.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [sharding](<https://devfeed.tech/topics/sharding.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [ai-research](<https://devfeed.tech/tags/ai-research.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [kv-cache](<https://devfeed.tech/tags/kv-cache.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llms](<https://devfeed.tech/tags/llms.md>), [parallelism](<https://devfeed.tech/tags/parallelism.md>), [performance](<https://devfeed.tech/tags/performance.md>), [sharding](<https://devfeed.tech/tags/sharding.md>)

## AI overview

Meta describes three forms of parallelism--tensor, context, and expert parallelism--for scaling large language model inference across GPUs. The article explains how prefill and decoding differ computationally and how these techniques target resource efficiency, throughput, and latency.

## Source excerpt

At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App. We're sharing how we developed and implemented advanced parallelism techniques to optimize key performance metrics related to resource efficiency, throughput, and latency. The rapid evolution of large language models (LLMs) has ushered in a [...] Read More... The post Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism appeared first on Engineering at Meta.