diff --git a/tools/launcher/common/query.py b/tools/launcher/common/query.py
index b9ee58b2903..27c41953d8a 100644
--- a/tools/launcher/common/query.py
+++ b/tools/launcher/common/query.py
@@ -94,10 +94,14 @@ def generate(self, messages, verbose=False, **chat_template_kwargs):
 parser.add_argument("--data-split", type=str, default="train", help="HF dataset split")
 parser.add_argument("--save", type=str, default=None, help="path to store the generated output.")
 parser.add_argument("--num-shards", type=int, default=1000, help="number of shards.")
+parser.add_argument("--shard-id", type=int, default=None, help="single shard id to process.")
 parser.add_argument("--shard-id-begin", type=int, default=0, help="the shard id to start.")
 parser.add_argument(
     "--shard-id-step", type=int, default=1, help="the step that the shard id progress."
 )
+parser.add_argument(
+    "--num-samples", "--num_samples", type=int, default=None, help="maximum samples to process."
+)
 parser.add_argument("--num-proc", type=int, default=32, help="number of processes (concurrency).")
 parser.add_argument("--temperature", type=float, default=0.0, help="temperature.")
 parser.add_argument(
@@ -207,26 +211,51 @@ def synthesize(data):
 else:
     dataset = load_dataset(args.data, split=args.data_split)
 
-if args.num_shards * 100 > len(dataset):
+if args.shard_id is None and args.num_shards * 100 > len(dataset):
     args.num_shards = max(1, min(16, len(dataset) // 100))
 
+# Apply --num-samples globally BEFORE sharding so the cap bounds total output,
+# not per-shard output (coderabbit:query.py:241).
+if args.num_samples is not None:
+    dataset = dataset.select(range(min(args.num_samples, len(dataset))))
+
+# Validate --shard-id once at the interface boundary (coderabbit:query.py:225).
+# dataset.shard(index=...) raises a confusing ValueError on out-of-range ids;
+# fail loud with a clear message instead.
+if args.shard_id is not None and not (0 <= args.shard_id < args.num_shards):
+    parser.error(f"--shard-id {args.shard_id} out of range [0, {args.num_shards})")
+
 if args.save is not None:
     print(f"Create save dir: {args.save}")
     os.makedirs(args.save, exist_ok=True)
 
-for shard_id in range(args.shard_id_begin, args.num_shards, args.shard_id_step):
-    file_path = args.save + f"/train-{shard_id + 1:05}-{args.num_shards:05}.jsonl"
+shard_ids = (
+    [args.shard_id]
+    if args.shard_id is not None
+    else range(args.shard_id_begin, args.num_shards, args.shard_id_step)
+)
+
+for shard_id in shard_ids:
+    if args.shard_id is None:
+        file_path = args.save + f"/train-{shard_id + 1:05}-{args.num_shards:05}.jsonl"
+        done_path = f"{file_path}.done"
+    else:
+        file_path = args.save + f"/shard_{shard_id}.jsonl"
+        done_path = args.save + f"/shard_{shard_id}.done"
 
-    if os.path.exists(file_path):
+    if os.path.exists(file_path) and os.path.exists(done_path):
         continue
 
     shard = dataset.shard(num_shards=args.num_shards, index=shard_id)
     print(len(shard), file_path)
 
+    num_proc = min(args.num_proc, len(shard))
     if shard_id % 2 == 0:
-        shard = shard.map(disable_thinking_column, num_proc=args.num_proc)
-    updated_shard = shard.map(synthesize, num_proc=args.num_proc)
+        shard = shard.map(disable_thinking_column, num_proc=num_proc)
+    updated_shard = shard.map(synthesize, num_proc=num_proc)
     updated_shard.to_json(file_path)
+    with open(done_path, "w") as done_file:
+        done_file.write("done\n")
     print(updated_shard[0])
 
     if early_termination:
diff --git a/tools/launcher/examples/Qwen/Qwen3-8B/hf_synth.yaml b/tools/launcher/examples/Qwen/Qwen3-8B/hf_synth.yaml
new file mode 100644
index 00000000000..9d19d512684
--- /dev/null
+++ b/tools/launcher/examples/Qwen/Qwen3-8B/hf_synth.yaml
@@ -0,0 +1,43 @@
+# Standalone vLLM data synthesis for Qwen3-8B.
+#
+# Usage:
+#   uv run slurm.py --yaml modules/Model-Optimizer/tools/launcher/examples/Qwen/Qwen3-8B/hf_synth.yaml --yes
+
+job_name: qwen3-8b-synth
+pipeline:
+  global_vars:
+    hf_model: /hf-local/Qwen/Qwen3-8B
+    output_dir: /scratchspace/modelopt/qwen3-8b-synth-v1
+
+  task_0:
+    script: common/vllm/query.sh
+    args:
+      - --model
+      - <<global_vars.hf_model>>
+      - --tensor-parallel-size
+      - "8"
+      - --trust-remote-code
+      - --enforce-eager
+      - --gpu-memory-utilization
+      - "0.95"
+      - --max-model-len
+      - "4096"
+      - --
+      - --data
+      - nvidia/Speculative-Decoding-Multilingual-Prompt-v2
+      - --save
+      - <<global_vars.output_dir>>
+      - --shard-id
+      - $SLURM_ARRAY_TASK_ID
+      - --num-shards
+      - "16"
+    environment:
+      - VLLM_STARTUP_TIMEOUT: "1800"
+    slurm_config:
+      _factory_: "slurm_factory"
+      nodes: 1
+      ntasks_per_node: 1
+      gpus_per_node: 8
+      container: vllm/vllm-openai:latest
+      array: "0-15"
+      requeue: true
diff --git a/tools/launcher/slurm_config.py b/tools/launcher/slurm_config.py
index 9c3c853e877..d8cb8ea90dc 100644
--- a/tools/launcher/slurm_config.py
+++ b/tools/launcher/slurm_config.py
@@ -45,6 +45,7 @@ class SlurmConfig:
     container_mounts: Optional[list[str]] = None
     srun_args: Optional[list[str]] = None
     array: Optional[str] = None
+    requeue: bool = False
     nodes: int = 1
     ntasks_per_node: int = 1
     gpus_per_node: int = 1
@@ -74,6 +75,7 @@ def slurm_factory(
     ],
     srun_args: list[str] = ["--no-container-mount-home"],
     array: Optional[str] = None,
+    requeue: bool = False,
     time: str = "04:00:00",
     segment: Optional[int] = None,
 ) -> SlurmConfig:
@@ -91,6 +93,7 @@ def slurm_factory(
         container_mounts=container_mounts,
         srun_args=srun_args,
         array=array,
+        requeue=requeue,
         time=time,
         segment=segment,
     )