diff --git a/nanochat/gpt.py b/nanochat/gpt.py
index 81ccb0c..5f00bc2 100644
--- a/nanochat/gpt.py
+++ b/nanochat/gpt.py
@@ -29,7 +29,21 @@ os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1"
 # Official docs of FA3 label it as "beta" and want you to install FA3 from source, which is a pain.
 # Wishing for official FA3 wheels soon, for now this seems to be a fast way to get them (ty varunneal)
 from kernels import get_kernel
-flash_attn = get_kernel('varunneal/flash-attention-3').flash_attn_interface
+
+flash_attn = None
+try:
+    # Flash Attention 3 uses NVIDIA Hopper-specific features like TMA (Tensor Memory Accelerator).
+    # These are only physically available on GPUs with Compute Capability >= 9.0 (e.g. H100).
+    # We explicitly check for this to prevent "No kernel image available" crashes on Ampere/Ada GPUs (RTX 30xx/40xx) etc.
+    if torch.cuda.is_available():
+        if torch.cuda.get_device_capability()[0] >= 9:
+            flash_attn = get_kernel('varunneal/flash-attention-3').flash_attn_interface
+        else:
+            # If the kernel image is not available, try installing the wheel manually from https://windreamer.github.io/flash-attention3-wheels/
+            import flash_attn_interface as flash_attn
+except Exception:
+    # Fallback to PyTorch SDPA on non-Hopper NVIDIA GPUs, Mac (MPS), or CPU.
+    pass
 
 @dataclass
 class GPTConfig:
@@ -92,17 +106,54 @@ class CausalSelfAttention(nn.Module):
         # 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
-            y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=window_size)
+            if flash_attn is not None:
+                y = flash_attn.flash_attn_func(q, k, v, causal=True, window_size=window_size)
+            else:
+                # Fallback Path (CPU/MPS): Needs Transpose to (B, H, T, D) for SDPA
+                y = F.scaled_dot_product_attention(
+                    q.transpose(1, 2), 
+                    k.transpose(1, 2), 
+                    v.transpose(1, 2), 
+                    is_causal=True
+                )
+                y = y.transpose(1, 2) # Restore layout to (B, T, H, D)
         else:
             # Inference: use flash_attn_with_kvcache which handles cache management
             k_cache, v_cache = kv_cache.get_layer_cache(self.layer_idx)
-            y = flash_attn.flash_attn_with_kvcache(
-                q, k_cache, v_cache,
-                k=k, v=v,
-                cache_seqlens=kv_cache.cache_seqlens,
-                causal=True,
-                window_size=window_size,
-            )
+            
+            if flash_attn is not None:
+                # Optimized Path (Linux/CUDA with FA3)
+                y = flash_attn.flash_attn_with_kvcache(
+                    q, k_cache, v_cache,
+                    k=k, v=v,
+                    cache_seqlens=kv_cache.cache_seqlens,
+                    causal=True,
+                    window_size=window_size,
+                )
+            else:
+                # Fallback Path (macOS/MPS or CPU) - Manual Cache Update + SDPA
+                positions = kv_cache.cache_seqlens
+                # Update cache manually for the batch
+                for b in range(B):
+                    pos = positions[b].item()
+                    k_cache[b, pos:pos+T] = k[b]
+                    v_cache[b, pos:pos+T] = v[b]
+                
+                # Compute attention manually
+                y = torch.empty_like(q)
+                for b in range(B):
+                    pos = positions[b].item()
+                    
+                    # Fetch history + cast to q.dtype (MPS fix) + Transpose (CPU fix)
+                    k_curr = k_cache[b:b+1, :pos+T].to(dtype=q.dtype).transpose(1, 2)
+                    v_curr = v_cache[b:b+1, :pos+T].to(dtype=q.dtype).transpose(1, 2)
+                    q_curr = q[b:b+1].transpose(1, 2)
+                    
+                    # Standard Attention (is_causal=False because context is explicit)
+                    att_out = F.scaled_dot_product_attention(q_curr, k_curr, v_curr, is_causal=False)
+                    # Transpose back to (B, T, H, D) and store
+                    y[b] = att_out.transpose(1, 2)[0]
+
             # Advance position after last layer processes
             if self.layer_idx == kv_cache.n_layers - 1:
                 kv_cache.advance(T)
@@ -405,4 +456,4 @@ class GPT(nn.Module):
                 next_ids = torch.argmax(logits, dim=-1, keepdim=True)
             ids = torch.cat((ids, next_ids), dim=1)
             token = next_ids.item()
-            yield token
+            yield token
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