diff --git a/recipes/fishaudio/Fish-Speech-S2-Pro.md b/recipes/fishaudio/Fish-Speech-S2-Pro.md
index 91ac3fe03..2a88bd169 100644
--- a/recipes/fishaudio/Fish-Speech-S2-Pro.md
+++ b/recipes/fishaudio/Fish-Speech-S2-Pro.md
@@ -102,3 +102,30 @@ curl -X POST http://localhost:8091/v1/audio/speech \
   both `ref_audio` and `ref_text` parameters.
 - Memory usage: Model loads at ~48.3 GiB, peaks at ~48.9 GiB during inference
   headroom for video frames and audio caches.
+
+### 2x H100 80GB
+
+#### Environment
+
+- OS: Linux
+- Python: 3.10+
+- Required package: `fish-speech` (for DAC codec)
+- CUDA 12.8
+- vLLM version: 0.19.0+
+
+#### Command
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 vllm serve fishaudio/s2-pro --omni --port 8091 \
+    --deploy-config vllm_omni/deploy/fish_qwen3_omni_2gpu.yaml
+```
+
+This profile pins Stage0 (Slow/Fast AR) to GPU 0 and Stage1 (DAC decoder)
+to GPU 1 to remove AR/DAC contention, sets `max_num_seqs=64` on both
+stages so concurrencies above 8 don't queue, and reduces
+`connector_get_sleep_s` from 10 ms to 1 ms so DAC chunks aren't held in
+the connector once they're ready.
+
+For benchmark numbers see the discussion in
+vllm-project/vllm-omni#2515 (initial H100x2 sweep by @Sy0307) and
+vllm-project/vllm-omni#3323 (H20x2 sweep with the shipped config).
diff --git a/vllm_omni/deploy/fish_qwen3_omni_2gpu.yaml b/vllm_omni/deploy/fish_qwen3_omni_2gpu.yaml
new file mode 100644
index 000000000..9fec34d69
--- /dev/null
+++ b/vllm_omni/deploy/fish_qwen3_omni_2gpu.yaml
@@ -0,0 +1,72 @@
+# Fish Speech S2 Pro 2-GPU deploy: Stage0 AR on GPU 0, Stage1 DAC on GPU 1.
+#
+# Opt-in profile validated on H100x2. Compared with the single-GPU default
+# (``fish_qwen3_omni.yaml``), it splits AR and DAC across devices to remove
+# AR/DAC contention, enlarges Stage0 AR batching, and lets Stage1 consume
+# multiple DAC chunks in parallel.
+#
+# Select via:
+#   CUDA_VISIBLE_DEVICES=0,1 vllm serve fishaudio/s2-pro --omni \
+#       --deploy-config vllm_omni/deploy/fish_qwen3_omni_2gpu.yaml
+#
+# Or programmatically:
+#   Omni(model="fishaudio/s2-pro",
+#        deploy_config_path="vllm_omni/deploy/fish_qwen3_omni_2gpu.yaml")
+
+async_chunk: true
+enable_chunked_prefill: false
+
+connectors:
+  connector_of_shared_memory:
+    name: SharedMemoryConnector
+    extra:
+      shm_threshold_bytes: 65536
+      codec_streaming: true
+      # Tightened from 10ms to 1ms: chunks become available sooner under
+      # cross-GPU pipelining, so connector idle waiting dominates if left high.
+      connector_get_sleep_s: 0.001
+      connector_get_max_wait_first_chunk: 3000
+      connector_get_max_wait: 300
+      # ~21 Hz codec; 25 frames ≈ 1.16s of audio.
+      codec_chunk_frames: 25
+      codec_left_context_frames: 25
+      initial_codec_chunk_frames: 4
+
+stages:
+  - stage_id: 0
+    max_num_seqs: 64
+    gpu_memory_utilization: 0.75
+    enforce_eager: false
+    async_scheduling: false
+    # vLLM scheduler requires max_num_batched_tokens >= max_model_len when
+    # chunked prefill is off (SlowAR decode loop isn't chunked-prefill-safe).
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    devices: "0"
+    output_connectors:
+      to_stage_1: connector_of_shared_memory
+    default_sampling_params:
+      temperature: 0.8
+      top_k: 30
+      top_p: 0.9
+      max_tokens: 2048
+      seed: 42
+      repetition_penalty: 1.0
+
+  - stage_id: 1
+    max_num_seqs: 64
+    gpu_memory_utilization: 0.2
+    enforce_eager: true
+    async_scheduling: false
+    max_num_batched_tokens: 16384
+    max_model_len: 16384
+    devices: "1"
+    input_connectors:
+      from_stage_0: connector_of_shared_memory
+    default_sampling_params:
+      temperature: 0.0
+      top_p: 1.0
+      top_k: -1
+      max_tokens: 65536
+      seed: 42
+      repetition_penalty: 1.0