Update deepseek_v32.md (#12296)

docs/basic_usage/deepseek_v32.md

@@ -50,15 +50,14 @@ python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --ep
 ### Configuration Tips
 - **DP Attention**: For DeepSeek V3.2 model, the kernels are customized for the use case of `dp_size=8`, so DP attention is enabled by default for better stability and performance. The feature of launching with pure TP is still under development.
 - **Choices of Attention Kernels**: The attention backend is automatically set to `nsa` attention backend for DeepSeek V3.2 model. In this backend, different kernels for sparse prefilling/decoding are implemented, which can be specified by `--nsa-prefill-backend` and `--nsa-decode-backend` server arguments. The choices of nsa prefill/decode attention kernels include:
-  - `flashmla_sparse`: `flash_mla_sparse_fwd` kernel from `flash_mla` library. Can run on both Hopper and Blackwell GPUs.
-  - `flashmla_kv`: `flash_mla_with_kvcache` kernel from `flash_mla` library. Can run on both Hopper and Blackwell GPUs.
-  - `fa3`: `flash_attn_with_kvcache` kernel from `flash_attn` library. Can only run on Hopper GPUs.
+  - `flashmla_sparse`: `flash_mla_sparse_fwd` kernel from `flash_mla` library. Can run on both Hopper and Blackwell GPUs. It requires bf16 q, kv inputs.
+  - `flashmla_kv`: `flash_mla_with_kvcache` kernel from `flash_mla` library. Can run on both Hopper and Blackwell GPUs. It requires bf16 q, fp8 k_cache inputs.
+  - `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.
   - `alter`: Alter kernel on AMD HPUs. Can only be used as decode kernel.
 - On the basis of performance benchmarks, the default configuration on H200 and B200 are set as follows :
   - H200: `flashmla_sparse` prefill attention, `fa3` decode attention, `bf16` kv cache dtype.
-  - B200: `flashmla_kv` prefill attention, `flashmla_kv` decode attention, `fp8_e4m3` kv cache dtype.
-  - Currently we don't enable `prefill=flashmla_sparse` with `decode=flashmla_kv` due to latency caused by kv cache quantization operations. In the future we might shift to this setting after attention/quantization kernels are optimized.
+  - B200: `flashmla_auto` prefill attention, `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.
 
 ## Multi-token Prediction
 SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on [EAGLE speculative decoding](https://docs.sglang.ai/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.