diff --git a/python/sglang/srt/environ.py b/python/sglang/srt/environ.py
index 1ebba7547..46e9c70dc 100644
--- a/python/sglang/srt/environ.py
+++ b/python/sglang/srt/environ.py
@@ -1344,6 +1344,8 @@ class Envs:
     # set False to fall back to the per-image loop.
     SGLANG_VIT_ENABLE_VECTORIZED_POS_EMBED = EnvBool(True)
     SGLANG_MM_SKIP_COMPUTE_HASH = EnvBool(False)
+    # Currently supported by the Kimi-K2.5 image processor only.
+    SGLANG_FORCE_CPU_IMAGE_PREPROCESSING = EnvBool(False)
     # For pre-tokenized (list[int]) multimodal prompts,
     # preserve the user's original tokens to avoid retokenization drift.
     SGLANG_MM_AVOID_RETOKENIZE = EnvBool(True)
diff --git a/python/sglang/srt/multimodal/processors/kimi_k25.py b/python/sglang/srt/multimodal/processors/kimi_k25.py
index 0653a6d1d..b477589f2 100644
--- a/python/sglang/srt/multimodal/processors/kimi_k25.py
+++ b/python/sglang/srt/multimodal/processors/kimi_k25.py
@@ -9,6 +9,7 @@ import torch.nn.functional as F
 from PIL import Image
 
 from sglang.kernels.ops.mm.process import normalize_and_patchify
+from sglang.srt.environ import envs
 from sglang.srt.managers.schedule_batch import (
     MultimodalProcessorOutput,
 )
@@ -25,6 +26,8 @@ from sglang.srt.multimodal.transport.cuda_ipc import (
 )
 from sglang.srt.runtime_context import get_mm
 
+_FORCE_CPU_IMAGE_PREPROCESSING = envs.SGLANG_FORCE_CPU_IMAGE_PREPROCESSING.get()
+
 # ---------------------------------------------------------------------------
 # GPU image preprocessing utilities (resize, pad, normalize, patchify on CUDA)
 # ---------------------------------------------------------------------------
@@ -389,7 +392,7 @@ class KimiGPUProcessorWrapper:
         images = images or kwargs.pop("images", None)
         original_input_ids = kwargs.pop("sglang_original_input_ids", None)
 
-        if images and torch.cuda.is_available():
+        if images and not _FORCE_CPU_IMAGE_PREPROCESSING and torch.cuda.is_available():
             return self._gpu_call(text, images, original_input_ids)
         return self._cpu_call(text, images, original_input_ids, **kwargs)
 
@@ -510,7 +513,7 @@ class KimiGPUProcessorWrapper:
 # Compatible with KimiVLForConditionalGeneration
 class KimiK2_5VLImageProcessor(KimiGridMMDataMixin, SGLangBaseProcessor):
     models = [KimiK25ForConditionalGeneration]
-    gpu_image_decode = True  # nvJPEG for JPEG, PIL fallback for others
+    gpu_image_decode = not _FORCE_CPU_IMAGE_PREPROCESSING
     prefer_tokenized_input = True
     precompute_hash_before_cpu_transfer = True
     # The GPU wrapper expands placeholders from the request's own token IDs.