This guide shows how to use Context Parallel, a long sequence inference optimization technique. Context Parallel includes `PCP` (Prefill Context Parallel) and `DCP` (Decode Context Parallel), which reduces NPU memory usage and improves inference speed in long sequence LLM inference.
Context parallel mainly solves the problem of serving long context requests. As prefill and decode present quite different characteristics and have quite different SLO (service level objectives), we need to implement context parallel separately for them. The major considerations are:
- For long context prefill, we can use context parallel to reduce TTFT (time to first token) by amortizing the computation time of the prefill across query tokens.
- For long context decode, we can use context parallel to reduce KV cache duplication and offer more space for KV cache to increase the batch size (and hence the throughput).
To learn more about the theory and implementation details of context parallel, please refer to the [context parallel developer guide](../../developer_guide/feature_guide/context_parallel.md).
- While using Context Parallel in KV cache transfer-needed scenario (e.g. KV pooling, PD disaggregation), to simplify KV cache transmission, `cp_kv_cache_interleave_size` must be set to the same value of KV cache `block_size`(default: 128), which specifies CP to split KV cache in a block-interleave style. For example:
To evaluate the effectiveness of Context Parallel in long sequence LLM inference scenarios, we use **DeepSeek-R1-W8A8** and **Qwen3-235B**, deploy PD disaggregate instances in the environment of 64 cards Ascend 910C*64G (A3), the configuration and performance data are as follows.
