Attention Backend

Attention Backend#

SGLang supports a large variety of attention backends. Each of them has different pros and cons. You can test them according to your needs.

Important

Selecting an optimal attention backend is crucial for maximizing your performance. Different backends excel in various scenarios, so choose based on your model, hardware, and use case. Not all backends are supported on all platforms and model architectures.

Support Matrix#

The support matrix is split into two parts: MHA (standard attention) and MLA (multi-head latent attention). For an explanation of the key differences between MHA and MLA, please see the SGLang documentation on DeepSeek MLA and the original DeepSeek MLA paper.

MHA Backends#

Backend

Page Size > 1 (native)

FP8 KV Cache

Spec topk=1

Spec topk>1

Sliding Window

MultiModal

FlashInfer

✅

✅

✅

✅

✅

❌

FA3 (FlashAttention 3)

✅

✅

✅

✅

✅

✅

FA4 (FlashAttention 4)

128

❌

❌

❌

❌

❌

Triton

❌

❌

✅

✅

✅

✅

Torch Native (SDPA)

❌

❌

❌

❌

❌

✅

FlexAttention (PyTorch)

❌

❌

❌

❌

❌

❌

TRTLLM MHA

16, 32 or 64

✅

✅

❌

✅

❌

Dual Chunk FlashAttention

✅

❌

❌

❌

❌

❌

AITER (ROCm)

✅

❌

✅

✅

❌

✅

Wave (ROCm)

✅

❌

❌

❌

❌

❌

Ascend (NPU)

✅

❌

❌

❌

❌

✅

Intel XPU

✅

❌

❌

❌

✅

❌

MLA Backends#

Backend

Native Page Sizes

FP8 KV Cache

Chunked Prefix Cache

Spec topk=1

Spec topk>1

FlashInfer MLA

1

❌

✅

✅

❌

FlashMLA

64

❌

✅

✅

❌

Cutlass MLA

128

✅

✅

✅

❌

TRTLLM MLA (Blackwell)

32 or 64

✅

✅

✅

❌

FA3 (FlashAttention 3)

n/a

❌

✅

✅

⚠️ (page_size=1 only)

Triton

n/a

❌

❌

✅

⚠️ (page_size=1 only)

FA4

128

❌

❌

❌

❌

Ascend MLA (NPU)

128

❌

❌

❌

❌

Note

Multimodal attention is selected by --mm-attention-backend. The “MultiModal” column indicates whether a corresponding multimodal implementation exists for that backend family.

Warning

FlashMLA FP8 KV cache is currently not working. See upstream issue #8856. Use non-FP8 KV or another backend when FP8 KV cache is required.

Note

  • FlashAttention 4 is prefill-only for now.

  • NSA is specifically designed for DeepSeek V3.2 DSA.

Tip

Speculative decoding topk: topk is the number of draft tokens sampled per step from the draft model. topk = 1 follows classic EAGLE; topk > 1 explores multiple branches and requires backend support in both draft and verification paths.

Note: Many backends that do not natively operate on pages can emulate page_size > 1 at the wrapper layer by expanding page tables to per-token indices. The “Page Size > 1 (native)” column indicates true in-kernel paging. Some backends require fixed native page sizes and cannot be reduced/emulated differently: TRTLLM MHA (16/32/64), TRTLLM MLA (32/64), FlashMLA (64), Cutlass MLA (128), FA4 (128), Ascend (128).

MLA page-size constraints:

  • FlashInfer MLA: page_size = 1.

  • FlashMLA: page_size = 64.

  • Cutlass MLA: page_size = 128.

  • TRTLLM MLA: page_size ∈ {32, 64}.

  • FA4: page_size = 128.

Hybrid attention (different backends for prefill vs decode) (Experimental)#

Warning

Hybrid attention is an experimental feature.

You can mix-and-match attention backends for prefill and decode. This is useful when one backend excels at prefill and another excels at decode. For the implementation details, please see python/sglang/srt/layers/attention/hybrid_attn_backend.py.

# Example: Prefill with FA4, Decode with TRTLLM MLA (Blackwell)
python3 -m sglang.launch_server \
  --model-path nvidia/DeepSeek-R1-FP4 \
  --tp 8 \
  --attention-backend trtllm_mla \
  --moe-runner-backend flashinfer_trtllm \
  --quantization modelopt_fp4 \
  --prefill-attention-backend fa4

Speculative decoding with hybrid attention#

Hybrid attention also works with speculative decoding. The backend used for draft decoding and target verification depends on --speculative-attention-mode:

  • --speculative-attention-mode decode (recommended): draft/verify use the decode backend.

  • --speculative-attention-mode prefill (default): draft/verify use the prefill backend.

Constraints when combining hybrid attention with speculative decoding:

  • If any attention backend is trtllm_mha, speculative decoding supports only --speculative-eagle-topk 1.

  • For paged MHA backends with --page-size > 1 and --speculative-eagle-topk > 1, only flashinfer is supported.

  • flex_attention is not supported with speculative decoding.

  • For MLA backends, trtllm_mla supports topk > 1; flashmla and flashinfer_mla support only topk = 1.

  • CUDA Graph: the decode backend is always captured; the prefill backend is captured only when --speculative-attention-mode prefill.

Tip

If you set only one of --prefill-attention-backend or --decode-attention-backend, the unspecified phase inherits --attention-backend. If both are specified and differ, SGLang automatically enables a hybrid wrapper to dispatch to the chosen backend per phase.

User Guide#

Launch Command for Different Attention Backends#

  • FlashInfer (Default for Non-Hopper Machines, e.g., A100, A40)

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend flashinfer
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-V3 \
  --attention-backend flashinfer \
  --trust-remote-code

  • FlashAttention 3 (Default for Hopper Machines, e.g., H100, H200, H20)

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend fa3
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-V3 \
  --trust-remote-code \
  --attention-backend fa3

  • Triton

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend triton
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-V3 \
  --attention-backend triton \
  --trust-remote-code

  • Torch Native

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend torch_native

  • FlashMLA

python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend flashmla \
  --trust-remote-code
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend flashmla \
  --kv-cache-dtype fp8_e4m3 \
  --trust-remote-code

  • TRTLLM MLA (Optimized for Blackwell Architecture, e.g., B200)

python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend trtllm_mla \
  --trust-remote-code

  • TRTLLM MLA with FP8 KV Cache (Higher concurrency, lower memory footprint)

python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend trtllm_mla \
  --kv-cache-dtype fp8_e4m3 \
  --trust-remote-code

  • Ascend

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend ascend

  • Intel XPU

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend intel_xpu

  • Wave

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend wave

  • FlexAttention

python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend flex_attention

  • Dual Chunk FlashAttention

python3 -m sglang.launch_server \
  --model Qwen/Qwen2.5-14B-Instruct-1M \
  --attention-backend dual_chunk_flash_attn

  • Cutlass MLA

python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend cutlass_mla \
  --trust-remote-code

  • FlashAttention 4 (MHA & MLA)

python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --prefill-attention-backend fa4 \
  --trust-remote-code

Steps to add a new attention backend#

To add a new attention backend, you can learn from the existing backends (python/sglang/srt/layers/attention/triton_backend.py, python/sglang/srt/layers/attention/flashattention_backend.py) and follow the steps below.

  1. Run without cuda graph. Support the two forward functions

    • forward_extend

      • Will be used for prefill, prefill with KV cache, and target verification

      • It will be called once per layer

    • forward_decode

      • Will be used for normal decode, and draft decode

      • It will be called once per layer

    • init_forward_metadata

      • Initialize the class and common metadata shared by all layers

      • Call the plan function for optimizations like split_kv

      • It will be called once per forward

  2. Run with cuda graph. It has two phases (capture and replay) and you need to implement three functions

    • init_cuda_graph_state

      • It will be called once during life time

      • Create all common shared buffers

    • init_forward_metadata_capture_cuda_graph

      • It will be called before capturing a cuda graph

      • It is similar to init_forward_metadata but write the medatada to some pre-defined buffers

    • init_forward_metadata_replay_cuda_graph

      • It will be called before replaying a cuda graph

      • This function is in the critical path and needs to be fast

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