diff --git a/nanochat/flash_attention.py b/nanochat/flash_attention.py
index 69b7144..c79d124 100644
--- a/nanochat/flash_attention.py
+++ b/nanochat/flash_attention.py
@@ -2,7 +2,7 @@
 Unified Flash Attention interface with automatic FA3/SDPA switching.
 
 Exports `flash_attn` module that matches the FA3 API exactly, but falls back
-to PyTorch SDPA on non-Hopper GPUs (including Blackwell), MPS, and CPU.
+to PyTorch SDPA on incompatible CUDA GPUs, MPS, and CPU.
 
 Usage (drop-in replacement for FA3):
     from nanochat.flash_attention import flash_attn
diff --git a/nanochat/gpt.py b/nanochat/gpt.py
index 208acd1..39c1d60 100644
--- a/nanochat/gpt.py
+++ b/nanochat/gpt.py
@@ -22,7 +22,7 @@ import torch.nn.functional as F
 from nanochat.common import get_dist_info, print0
 from nanochat.optim import MuonAdamW, DistMuonAdamW
 
-# Our custom Flash Attention module that automatically uses FA3 on Hopper+ and SDPA fallback elsewhere
+# Our custom Flash Attention module that automatically uses FA3 when compatible and SDPA fallback otherwise
 from nanochat.flash_attention import flash_attn
 
 @dataclass
@@ -93,7 +93,7 @@ class CausalSelfAttention(nn.Module):
         q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin)
         q, k = norm(q), norm(k) # QK norm
 
-        # Flash Attention (FA3 on Hopper+, PyTorch SDPA fallback elsewhere)
+        # Flash Attention (FA3 or SDPA fallback)
         # window_size is (left, right) tuple: (N, 0) for causal, (-1, 0) for full context
         if kv_cache is None:
             # Training: causal attention with optional sliding window
diff --git a/scripts/base_train.py b/scripts/base_train.py
index 9be4b6b..4d17638 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -96,7 +96,7 @@ wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat",
 
 # Flash Attention status
 if HAS_FA3:
-    print0("✓ Using Flash Attention 3 (Hopper GPU detected), efficient, new and awesome.")
+    print0("✓ Using Flash Attention 3: efficient, new and awesome.")
 else:
     print0("!" * 80)
     print0("WARNING: Flash Attention 3 not available, using PyTorch SDPA fallback")
diff --git a/tests/test_attention_fallback.py b/tests/test_attention_fallback.py
index 9741c7f..5c011dc 100644
--- a/tests/test_attention_fallback.py
+++ b/tests/test_attention_fallback.py
@@ -7,8 +7,8 @@ Note on test structure:
     Tests are split into two classes due to dtype/device constraints:
 
     1. TestFA3VsSDPA: Comparison tests that run both FA3 and SDPA on the same inputs
-       and verify they produce identical results. These require a Hopper GPU (FA3 only
-       works on sm90+) and use bfloat16 (FA3 doesn't support float32).
+       and verify they produce identical results. These require a compatible GPU (FA3 only
+       works on sm80 and sm90) and use bfloat16 (FA3 doesn't support float32).
 
     2. TestSDPAOnly: Tests that only exercise the SDPA fallback path. These can run
        on any device (CUDA, CPU, MPS) with the appropriate dtype for that device.
@@ -45,11 +45,11 @@ def assert_close(t1, t2, name, atol=1e-2, rtol=1e-2):
 
 
 # =============================================================================
-# FA3 vs SDPA comparison tests (require Hopper GPU)
+# FA3 vs SDPA comparison tests
 # =============================================================================
 @pytest.mark.skipif(not HAS_FA3, reason="FA3 required to compare implementations")
 class TestFA3VsSDPA:
-    """Compare FA3 and SDPA produce identical results. Requires Hopper GPU."""
+    """Compare FA3 and SDPA produce identical results."""
 
     DEVICE = "cuda"
     DTYPE = torch.bfloat16