diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py
index 33d486263..ef71a05d3 100644
--- a/vllm/config/vllm.py
+++ b/vllm/config/vllm.py
@@ -14,7 +14,7 @@ from datetime import datetime
 from enum import IntEnum
 from functools import lru_cache
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, TypeVar, get_args
+from typing import TYPE_CHECKING, Any, Literal, TypeVar, get_args
 
 import torch
 from pydantic import ConfigDict, Field, model_validator
@@ -76,6 +76,8 @@ class OptimizationLevel(IntEnum):
     """O3: Currently the same as -O2s."""
 
 
+PerformanceMode = Literal["balanced", "interactivity", "throughput"]
+
 IS_QUANTIZED = False
 IS_DENSE = False
 # The optimizations that depend on these properties currently set to False
@@ -312,6 +314,13 @@ class VllmConfig:
     performance. -O2 is used by default. See OptimizationLevel for full
     description."""
 
+    performance_mode: PerformanceMode = "balanced"
+    """Performance mode for runtime behavior, 'balanced' is the default.
+    'interactivity' favors low end-to-end per-request latency at small batch
+    sizes (fine-grained CUDA graphs, latency-oriented kernels).
+    'throughput' favors aggregate tokens/sec at high concurrency (larger CUDA
+    graphs, more aggressive batching, throughput-oriented kernels)."""
+
     weight_transfer_config: WeightTransferConfig | None = None
     """The configurations for weight transfer during RL training."""
 
@@ -643,6 +652,11 @@ class VllmConfig:
         # To give each torch profile run a unique instance name.
         self.instance_id = f"{time.time_ns()}"
 
+        if self.performance_mode != "balanced":
+            logger.info_once(
+                "Performance mode set to '%s'.", self.performance_mode, scope="local"
+            )
+
         self.try_verify_and_update_config()
 
         if self.model_config is not None:
@@ -1332,9 +1346,15 @@ class VllmConfig:
                 # sort to make sure the sizes are in ascending order
                 cudagraph_capture_sizes.sort()
             else:
-                cudagraph_capture_sizes = [
-                    i for i in [1, 2, 4] if i <= max_cudagraph_capture_size
-                ]
+                if self.performance_mode == "interactivity":
+                    # Fine-grained CUDA graphs at small batch sizes
+                    # for minimal padding overhead
+                    interactivity_max = min(max_cudagraph_capture_size, 32)
+                    cudagraph_capture_sizes = list(range(1, interactivity_max + 1))
+                else:
+                    cudagraph_capture_sizes = [
+                        i for i in [1, 2, 4] if i <= max_cudagraph_capture_size
+                    ]
                 if max_cudagraph_capture_size >= 8:
                     # Step size 8 for small batch sizes, up to 256(not included)
                     cudagraph_capture_sizes += list(
@@ -1345,6 +1365,8 @@ class VllmConfig:
                     cudagraph_capture_sizes += list(
                         range(256, max_cudagraph_capture_size + 1, 16)
                     )
+                # de-duplicate and sort the sizes
+                cudagraph_capture_sizes = sorted(set(cudagraph_capture_sizes))
 
             if (
                 self.parallel_config.tensor_parallel_size > 1
diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py
index 15a662ba2..ca76454d6 100644
--- a/vllm/engine/arg_utils.py
+++ b/vllm/engine/arg_utils.py
@@ -89,7 +89,7 @@ from vllm.config.parallel import (
 )
 from vllm.config.scheduler import SchedulerPolicy
 from vllm.config.utils import get_field
-from vllm.config.vllm import OptimizationLevel
+from vllm.config.vllm import OptimizationLevel, PerformanceMode
 from vllm.logger import init_logger, suppress_logging
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.plugins import load_general_plugins
@@ -596,6 +596,7 @@ class EngineArgs:
 
     kv_sharing_fast_prefill: bool = CacheConfig.kv_sharing_fast_prefill
     optimization_level: OptimizationLevel = VllmConfig.optimization_level
+    performance_mode: PerformanceMode = VllmConfig.performance_mode
 
     kv_offloading_size: float | None = CacheConfig.kv_offloading_size
     kv_offloading_backend: KVOffloadingBackend = CacheConfig.kv_offloading_backend
@@ -1264,6 +1265,7 @@ class EngineArgs:
         vllm_group.add_argument(
             "--optimization-level", **vllm_kwargs["optimization_level"]
         )
+        vllm_group.add_argument("--performance-mode", **vllm_kwargs["performance_mode"])
         vllm_group.add_argument(
             "--weight-transfer-config", **vllm_kwargs["weight_transfer_config"]
         )
@@ -1894,6 +1896,7 @@ class EngineArgs:
             profiler_config=self.profiler_config,
             additional_config=self.additional_config,
             optimization_level=self.optimization_level,
+            performance_mode=self.performance_mode,
             weight_transfer_config=self.weight_transfer_config,
         )
 
@@ -2110,6 +2113,13 @@ class EngineArgs:
                 SchedulerConfig.DEFAULT_MAX_NUM_SEQS,
             )
 
+        # If throughput mode is set, double max_num_batched_tokens and max_num_seqs.
+        if self.performance_mode == "throughput":
+            if orig_max_num_batched_tokens is None:
+                self.max_num_batched_tokens *= 2
+            if orig_max_num_seqs is None:
+                self.max_num_seqs *= 2
+
         if orig_max_num_batched_tokens is None:
             assert model_config.max_model_len is not None, (
                 "max_model_len must be set by this point"