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[Docs] PD with Nixl compat matrix (#38628)

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Signed-off-by: NickLucche <nlucches@redhat.com>
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Nicolò Lucchesi
2026-03-31 17:01:21 +02:00
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docs/features/disagg_prefill.md
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- **ExampleConnector**: refer to [examples/offline_inference/disaggregated-prefill-v1/run.sh](../../examples/offline_inference/disaggregated-prefill-v1/run.sh) for the example usage of ExampleConnector disaggregated prefilling.
- **LMCacheConnectorV1**: refer to [examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh](../../examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh) for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
- **NixlConnector**: refer to [tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh](../../tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh) for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md).
- **NixlConnector**: refer to [tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh](../../tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh) for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md). For feature compatibility details, see [NixlConnector Compatibility Matrix](nixl_connector_compatibility.md).
- **P2pNcclConnector**: refer to [examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh](../../examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh) for the example usage of P2pNcclConnector disaggregated prefilling.
- **MooncakeConnector**: refer to [examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh](../../examples/online_serving/disaggregated_serving/mooncake_connector/run_mooncake_connector.sh) for the example usage of ExampleConnector disaggregated prefilling. For detailed usage guide, see [MooncakeConnector Usage Guide](mooncake_connector_usage.md).
- **MultiConnector**: take advantage of the kv_connector_extra_config: dict[str, Any] already present in KVTransferConfig to stash all the connectors we want in an ordered list of kwargs.such as:

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# NixlConnector Compatibility Matrix
This page documents the feature compatibility of **disaggregated prefilling with the NixlConnector**. For general usage instructions, see the [NixlConnector Usage Guide](nixl_connector_usage.md). For an overview of disaggregated prefilling, see [Disaggregated Prefilling](disagg_prefill.md).
!!! note
This page reflects the current state of the codebase and is subject to change as features evolve. Entries marked 🟠 or ❌ may link to tracking issues. See the [NIXL connector roadmap](https://github.com/vllm-project/vllm/issues/33702) for upcoming feature development.
**Legend:**
- ✅ = Fully supported
- 🟠 = Partial support (see footnotes)
- ❌ = Not supported
- ❔ = Unknown / not yet validated
- 🚧 = Work in progress
!!! info "Universally supported features"
The following features work with **all** model architectures when using NixlConnector PD disaggregated serving:
[Chunked Prefill](../configuration/optimization.md#chunked-prefill) |
[APC (Prefix Caching)](automatic_prefix_caching.md) |
[Data Parallel](../serving/data_parallel_deployment.md) |
CUDA graph |
Logprobs |
Prompt Logprobs |
[Prompt Embeds](prompt_embeds.md) |
Multiple NIXL backends (UCX, GDS, LIBFABRIC, etc.)
## Model Architecture x Capability
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| Model type | <abbr title="Basic Prefill/Decode disaggregation">Basic PD</abbr> | <abbr title="Speculative Decoding">Spec Decode</abbr> | <abbr title="Heterogeneous Tensor Parallelism (P TP != D TP)">Hetero TP</abbr> | <abbr title="Cross-layer blocks optimization">Cross-layer blocks</abbr> | <abbr title="Sliding Window Attention">SWA</abbr> | <abbr title="CPU host buffer offload (e.g. TPU)">Host buffer</abbr> | <abbr title="Different block sizes on P and D">Hetero block size</abbr> |
| - | - | - | - | - | - | - | - |
| Dense Transformers | ✅ | ✅<sup>1</sup> | ✅ | ✅<sup>2</sup> | ✅ | ✅ | 🟠<sup>3</sup> |
| MLA (e.g. DeepSeek-V2/V3) | ✅ | ✅<sup>1</sup> | 🟠<sup>4</sup> | ✅<sup>2</sup> | ✅ | ✅ | 🟠<sup>3</sup> |
| Sparse MLA (e.g. DeepSeek-V3.2) | ✅ | ✅<sup>1</sup> | 🟠<sup>4</sup> | ✅<sup>2</sup> | ✅ | ✅ | 🟠<sup>3</sup> |
| Hybrid SSM / Mamba | ✅ | ❔ | 🚧<sup>5</sup> | ❌ | ✅ | ✅ | ❌<sup>6</sup> |
| MoE | ✅ | ✅<sup>1</sup> | ✅ | ✅<sup>2</sup> | ✅ | ✅ | 🟠<sup>3</sup> |
| Multimodal | ❔ | ❔ | ❔ | ❔ | ❔ | ❔ | ❔ |
| Encoder-Decoder | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
<sup>1</sup> P and D instances must use the same speculation configuration.
<sup>2</sup> Requires `FLASH_ATTN` or `FLASHINFER` backend **and** `HND` KV cache layout. Enable via `--kv-transfer-config '{"kv_connector_extra_config": {"enable_cross_layers_blocks": "True"}}'`.
<sup>3</sup> Supported only when HMA is **not** required (i.e., non-hybrid models). Block IDs are remapped automatically. Only P block size < D block size is supported.
<sup>4</sup> MLA KV cache is replicated across TP workers, so heterogeneous TP works but there is no head-splitting. When P TP > D TP, only a single read is executed (redundant ranks are skipped). D TP > P TP also works.
<sup>5</sup> Hybrid SSM (Mamba) models require **homogeneous TP** (`P TP == D TP`). Heterogeneous TP is not yet supported for Mamba layers.
<sup>6</sup> HMA (required by hybrid models) does not support different remote block sizes.
## Configuration Notes
### What must match between P and D
By default, a **compatibility hash** is checked during handshake. P and D instances must agree on:
- vLLM version and NIXL connector version
- Model (architecture, dtype, number of KV heads, head size, number of hidden layers)
- Attention backend
- KV cache dtype (`cache_dtype`)
!!! warning
Disable the hash check with `--kv-transfer-config '{"kv_connector_extra_config": {"enforce_handshake_compat": false}}'` at your own risk.
### What can safely differ between P and D
- `tensor-parallel-size` (heterogeneous TP, subject to model restrictions above)
- `block-size` (heterogeneous block size, subject to restrictions above)
- Number of KV cache blocks (determined by available memory on each instance)
### KV cache layout
- NixlConnector defaults to **`HND`** layout for optimal transfer performance (non-MLA models).
- `NHD` layout is supported but does **not** allow heterogeneous TP head splitting.
- Experimental `HND` ↔ `NHD` permute: enable via `--kv-transfer-config '{"enable_permute_local_kv": true}'`. Not supported with HMA.
### Quantized KV cache
[Quantized KV cache](quantization/quantized_kvcache.md) (e.g., FP8) requires both P and D instances to use the **same** `cache_dtype`. Mismatched cache dtypes will fail the compatibility hash check during handshake.
- **Static quantization** (scales loaded from checkpoint): ✅ Supported. Scales are loaded independently by each instance from the model checkpoint.
- **Dynamic quantization** (scales computed at runtime): ❌ Not supported. Per-block scales are not transferred alongside KV cache data.
- **Packed-layout scales** (scales stored inline with weights): ✅ Supported. Scales are transferred together with the KV cache blocks.

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NixlConnector is a high-performance KV cache transfer connector for vLLM's disaggregated prefilling feature. It provides fully asynchronous send/receive operations using the NIXL library for efficient cross-process KV cache transfer.
For feature compatibility details (supported model architectures, TP configurations, and feature interactions), see the [NixlConnector Compatibility Matrix](nixl_connector_compatibility.md).
## Prerequisites
### Installation