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Nsa trtllm mla sparse fp8 support with Deepseek v3.2 NVFP4 (#18389)

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Rain Jiang
2026-02-15 17:29:54 -08:00
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4
docs/advanced_features/server_arguments.md
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@@ -266,8 +266,8 @@ Please consult the documentation below and [server_args.py](https://github.com/s
| `--sampling-backend` | Choose the kernels for sampling layers. | `None` | `flashinfer`, `pytorch`, `ascend` |
| `--grammar-backend` | Choose the backend for grammar-guided decoding. | `None` | `xgrammar`, `outlines`, `llguidance`, `none` |
| `--mm-attention-backend` | Set multimodal attention backend. | `None` | `sdpa`, `fa3`, `fa4`, `triton_attn`, `ascend_attn`, `aiter_attn` |
| `--nsa-prefill-backend` | Choose the NSA backend for the prefill stage (overrides `--attention-backend` when running DeepSeek NSA-style attention). | `flashmla_sparse` | `flashmla_sparse`, `flashmla_kv`, `flashmla_auto`, `fa3`, `tilelang`, `aiter` |
| `--nsa-decode-backend` | Choose the NSA backend for the decode stage when running DeepSeek NSA-style attention. Overrides `--attention-backend` for decoding. | `fa3` | `flashmla_sparse`, `flashmla_kv`, `fa3`, `tilelang`, `aiter` |
| `--nsa-prefill-backend` | Choose the NSA backend for the prefill stage (overrides `--attention-backend` when running DeepSeek NSA-style attention). | `flashmla_sparse` | `flashmla_sparse`, `flashmla_kv`, `flashmla_auto`, `fa3`, `tilelang`, `aiter`, `trtllm` |
| `--nsa-decode-backend` | Choose the NSA backend for the decode stage when running DeepSeek NSA-style attention. Overrides `--attention-backend` for decoding. | `fa3` | `flashmla_sparse`, `flashmla_kv`, `fa3`, `tilelang`, `aiter`, `trtllm` |
| `--fp8-gemm-backend` | Choose the runner backend for Blockwise FP8 GEMM operations. Options: 'auto' (default, auto-selects based on hardware), 'deep_gemm' (JIT-compiled; enabled by default on NVIDIA Hopper (SM90) and Blackwell (SM100) when DeepGEMM is installed), 'flashinfer_trtllm' (optimal for Blackwell and low-latency), 'cutlass' (optimal for Hopper/Blackwell GPUs and high-throughput), 'triton' (fallback, widely compatible), 'aiter' (ROCm only). **NOTE**: This replaces the deprecated environment variables SGLANG_ENABLE_FLASHINFER_FP8_GEMM and SGLANG_SUPPORT_CUTLASS_BLOCK_FP8. | `auto` | `auto`, `deep_gemm`, `flashinfer_trtllm`, `cutlass`, `triton`, `aiter` |
| `--fp4-gemm-backend` | Choose the runner backend for NVFP4 GEMM operations. Options: 'flashinfer_cutlass' (default), 'auto' (auto-selects between flashinfer_cudnn/flashinfer_cutlass based on CUDA/cuDNN version), 'flashinfer_cudnn' (FlashInfer cuDNN backend, optimal on CUDA 13+ with cuDNN 9.15+), 'flashinfer_trtllm' (FlashInfer TensorRT-LLM backend, requires different weight preparation with shuffling). All backends are from FlashInfer; when FlashInfer is unavailable, sgl-kernel CUTLASS is used as an automatic fallback. **NOTE**: This replaces the deprecated environment variable SGLANG_FLASHINFER_FP4_GEMM_BACKEND. | `flashinfer_cutlass` | `auto`, `flashinfer_cudnn`, `flashinfer_cutlass`, `flashinfer_trtllm` |
| `--disable-flashinfer-autotune` | Flashinfer autotune is enabled by default. Set this flag to disable the autotune. | `False` | bool flag (set to enable) |

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docs/basic_usage/deepseek_v32.md
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@@ -66,9 +66,13 @@ python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --n
- `fa3`: `flash_attn_with_kvcache` kernel from `flash_attn` library. Can only run on Hopper GPUs. It requires bf16 q, kv inputs.
- `tilelang`: `tilelang` implementation that can run on GPU, HPU and NPU.
- `aiter`: Aiter kernel on AMD HPUs. Can only be used as decode kernel.
- `trtllm`: `trtllm-mla` sparse kernel from flashinfer library. Only run on blackwell GPUs. It requires QKV bf16 or QKV fp8.
- On the basis of performance benchmarks, the default configuration on H200 and B200 are set as follows :
- H200: `flashmla_sparse` prefill attention (short-seq prefill uses MHA via FlashAttention varlen), `fa3` decode attention, `bf16` kv cache dtype.
- B200: `flashmla_auto` prefill attention (short-seq prefill uses MHA via TRT-LLM ragged), `flashmla_kv` decode attention, `fp8_e4m3` kv cache dtype. `flashmla_auto` enables automatic selection of either `flashmla_sparse` or `flashmla_kv` kernel for prefill based on KV cache dtype, hardware, and heuristics. When FP8 KV cache is enabled and `total_kv_tokens < total_q_tokens * 512`, it uses the `flashmla_sparse` kernel; otherwise, it falls back to the `flashmla_kv` kernel. The heuristics may need to be tuned if the performance of either the `flashmla_sparse` or `flashmla_kv` kernel changes significantly.
- On Blackwell platform, with slightly accuracy drop, the performance can boost up to 3x-5x
- B200: by choosing `trtllm` for both `--nsa-prefill-backend` and `--nsa-decode-backend`, the prefill attention use MHA via TRT-LLM ragged for both short and long sequence (**accuracy impact**). Combine the `trtllm` with `fp8_e4m3` kv cache, the kv cache dim is `576` (kv_lora_rank + qk_rope_head_dim) (**accuracy impact**), compare to the combination of `flashmla_auto` and `fp8_e4m` kv cache dim is `656` (kv_lora_rank + scale storage (kv_lora_rank // quant_block_size * 4 bytes) + rope dimension storage).
## Multi-token Prediction
SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved significantly on small batch sizes. Please look at [this PR](https://github.com/sgl-project/sglang/pull/11652) for more information.