[docs] Fix kernel name (#14887)

docs/basic_usage/deepseek_v32.md

@@ -56,7 +56,7 @@ python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8
   - `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.
+  - `aiter`: Aiter 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 (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.