Support compress-tensors with nvfp4 or fp8 weights and modelopt with nvfp4 weights on Turing (#33076)

Signed-off-by: IriKa Qiu <qiujie.jq@gmail.com>

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a16_nvfp4.py

@@ -29,7 +29,7 @@ class CompressedTensorsW4A16Fp4(CompressedTensorsScheme):
     @classmethod
     def get_min_capability(cls) -> int:
         # don't restrict as emulations
-        return 80
+        return 75
 
     def create_weights(
         self,

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py

@@ -34,8 +34,8 @@ class CompressedTensorsW8A16Fp8(CompressedTensorsScheme):
 
     @classmethod
     def get_min_capability(cls) -> int:
-        # ampere and up
-        return 80
+        # turing and up
+        return 75
 
     # W8A8-Fp8 kernels support only per-tensor and per-channel cases.
     # So if we have a fused module (QKV, MLP) with per tensor scales,

vllm/model_executor/layers/quantization/modelopt.py

@@ -96,6 +96,7 @@ from vllm.model_executor.parameter import (
     PerTensorScaleParameter,
 )
 from vllm.model_executor.utils import replace_parameter
+from vllm.platforms import current_platform
 from vllm.utils.flashinfer import (
     flashinfer_scaled_fp4_mm,
     has_flashinfer,
@@ -1110,7 +1111,7 @@ class ModelOptNvFp4LinearMethod(LinearMethodBase):
 
         self.backend = "none"
         if envs.VLLM_NVFP4_GEMM_BACKEND is None:
-            if has_flashinfer():
+            if current_platform.has_device_capability(100) and has_flashinfer():
                 self.backend = "flashinfer-cutlass"
             elif cutlass_fp4_supported():
                 self.backend = "cutlass"