diff --git a/docs/design/attention_backends.md b/docs/design/attention_backends.md
index 6ac2a2c863..4fee50068e 100644
--- a/docs/design/attention_backends.md
+++ b/docs/design/attention_backends.md
@@ -160,7 +160,7 @@ Priority is **1 = highest** (tried first).
 | Backend | Version | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | MM Prefix | DCP | Attention Types | Compute Cap. |
 | ------- | ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | --------- | --- | --------------- | ------------ |
 | `CPU_ATTN` | | fp16, bf16, fp32 | `auto`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256, 512 | ❌ | ❌ | ❌ | ❌ | All | N/A |
-| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ✅ | ❌ | ✅ | Decoder | 7.x-9.x |
+| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ✅ | ❌ | ✅ | Decoder | 8.x-9.x |
 | `FLASHINFER` | TRTLLM† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ✅ | ✅ | ❌ | ✅ | Decoder | 10.x |
 | `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | ✅ | All | ≥8.0 |
 | `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
diff --git a/vllm/model_executor/layers/quantization/modelopt.py b/vllm/model_executor/layers/quantization/modelopt.py
index 24ec55e400..d51a2dd312 100644
--- a/vllm/model_executor/layers/quantization/modelopt.py
+++ b/vllm/model_executor/layers/quantization/modelopt.py
@@ -2299,13 +2299,14 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfigBase):
 
     @classmethod
     def get_min_capability(cls) -> int:
-        # Ampere (SM80/SM86): NVFP4 routed experts run via Marlin W4A16, and FP8
-        # weight-only dense layers run via MarlinFP8 (W8A16, compute in
-        # bf16/fp16). FP8 MoE, if present, also routes to Marlin because
-        # TritonExperts gates its FP8 schemes behind supports_fp8() (cc>=89).
-        # None of these paths require native FP8 tensor cores, so SM80 is
-        # sufficient.
-        return 80
+        # Turing and up (SM75+): NVFP4 routed experts run via Marlin W4A16
+        # (SM75+), FP8 weight-only dense via MarlinFP8 (cc>=7.5), and FP8 MoE,
+        # if present, via Marlin (TritonExperts gates its FP8 schemes behind
+        # supports_fp8(), cc>=89). None of these paths require native FP8 tensor
+        # cores, so SM75 is sufficient. Validated end-to-end on a Tesla T4
+        # (SM75) and A100 (SM80). Pairs with the FlashInfer attention SM80
+        # lower bound so SM75 auto-selects a supported attention backend.
+        return 75
 
     @classmethod
     def override_quantization_method(
diff --git a/vllm/v1/attention/backends/flashinfer.py b/vllm/v1/attention/backends/flashinfer.py
index 044fc8c79c..80319003da 100755
--- a/vllm/v1/attention/backends/flashinfer.py
+++ b/vllm/v1/attention/backends/flashinfer.py
@@ -424,7 +424,12 @@ class FlashInferBackend(AttentionBackend):
 
     @classmethod
     def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
-        return capability >= DeviceCapability(7, 5) and capability <= DeviceCapability(
+        # FlashInfer supports SM75+, but is currently broken on SM75 (Turing):
+        # https://github.com/flashinfer-ai/flashinfer/issues/3620 (fix:
+        # https://github.com/flashinfer-ai/flashinfer/pull/3621). Temporarily
+        # raise the floor to SM80 so it is not auto-selected on SM75 until
+        # that fix lands; revert to DeviceCapability(7, 5) once it does.
+        return capability >= DeviceCapability(8, 0) and capability <= DeviceCapability(
             12, 1
         )