diff --git a/nanochat/mfu.py b/nanochat/mfu.py
new file mode 100644
index 0000000..157e68d
--- /dev/null
+++ b/nanochat/mfu.py
@@ -0,0 +1,91 @@
+import torch
+
+
+def get_promised_flops_per_gpu():
+    """
+    Return best-effort dense BF16 Tensor/Matrix peak FLOPs for the active GPU.
+    Returns:
+        tuple[str, float, bool]: (device_name, flops_per_gpu, is_estimated)
+            device_name is the CUDA-reported name for the active device.
+            flops_per_gpu is the per-device BF16 dense peak in FLOPs.
+            is_estimated is True when we fall back to heuristic defaults.
+    """
+    device_index = torch.cuda.current_device()
+    props = torch.cuda.get_device_properties(device_index)
+    name = props.name.lower()
+    device_name = props.name
+
+    def result(flops, estimated):
+        return device_name, flops, estimated
+
+    def has(*keywords: str) -> bool:
+        return any(k in name for k in keywords)
+
+    # --- NVIDIA Blackwell ---
+    if has("gb200", "grace blackwell"):
+        return result(2.5e15, False)       # GB200 dense BF16 ≈ 2.5 PFLOPS (5.0 PFLOPS sparse)
+    if has("b200"):
+        return result(2.25e15, False)      # B200 dense BF16 ≈ 2.25 PFLOPS (4.5 PFLOPS sparse)
+    if has("b100"):
+        return result(1.8e15, False)       # B100 dense BF16 ≈ 1.8 PFLOPS (3.5 PFLOPS sparse)
+
+    # --- NVIDIA Hopper (H100/H200/H800) ---
+    if has("h200"):
+        if has("nvl", "pcie"):
+            return result(836e12, False)   # H200 NVL/PCIe dense BF16 ≈ 836 TFLOPS
+        return result(989e12, False)       # H200 SXM dense BF16 ≈ 989 TFLOPS
+
+    if has("h100"):
+        if has("nvl"):
+            return result(835e12, False)   # H100 NVL dense BF16 ≈ 835 TFLOPS
+        if has("pcie"):
+            return result(756e12, False)   # H100 PCIe dense BF16 ≈ 756 TFLOPS
+        return result(989e12, False)       # H100 SXM dense BF16 ≈ 989 TFLOPS
+
+    if has("h800"):
+        if has("nvl"):
+            return result(989e12, False)   # H800 NVLink dense BF16 ≈ 989 TFLOPS
+        return result(756e12, False)       # H800 PCIe dense BF16 ≈ 756 TFLOPS
+
+    # --- NVIDIA Ampere data center / export variants ---
+    if has("a100", "pg506"):
+        return result(312e12, False)       # A100 SXM dense BF16 = 312 TFLOPS
+    if has("a800"):
+        return result(312e12, False)       # A800 dense BF16 ≈ 312 TFLOPS (Ampere-class)
+
+    # Useful Ada data-center cards
+    if has("l40s", "l40-s", "l40 s"):
+        return result(362e12, False)       # L40S dense BF16 ≈ 362 TFLOPS
+    if has(" l4", " l4 ", "nvidia l4", " l4-") or name.endswith(" l4"):
+        return result(121e12, False)       # L4 dense BF16 ≈ 121 TFLOPS
+
+    # Other widely used Ampere data-center cards
+    if has("a30"):
+        return result(165e12, False)       # A30 dense BF16 ≈ 165 TFLOPS
+    if has("a40"):
+        return result(149.7e12, False)     # A40 dense BF16 ≈ 149.7 TFLOPS
+
+    # --- AMD CDNA accelerators ---
+    if has("mi355"):
+        return result(2.5e15, False)       # MI355X dense BF16 ≈ 2.5 PFLOPS (5.0 PFLOPS sparse)
+    if has("mi325"):
+        return result(1.3074e15, False)    # MI325X dense BF16 ≈ 1.3074 PFLOPS
+    if has("mi300x"):
+        return result(1.3074e15, False)    # MI300X dense BF16 ≈ 1.3074 PFLOPS
+    if has("mi300a"):
+        return result(980.6e12, False)     # MI300A dense BF16 ≈ 980.6 TFLOPS
+    if has("mi250x"):
+        return result(383e12, False)       # MI250X dense BF16 ≈ 383 TFLOPS
+    if has("mi250"):
+        return result(362.1e12, False)     # MI250 dense BF16 ≈ 362.1 TFLOPS
+
+    # --- Consumer RTX ---
+    if has("5090"):
+        return result(209.5e12, False)     # RTX 5090 dense BF16 ≈ 209.5 TFLOPS (w/ FP32 accumulate)
+    if has("4090"):
+        return result(165.2e12, False)     # RTX 4090 dense BF16 ≈ 165.2 TFLOPS (w/ FP32 accumulate)
+    if has("3090"):
+        return result(71e12, False)        # RTX 3090 dense BF16 ≈ 71 TFLOPS (w/ FP32 accumulate)
+
+    # unknown
+    return result(1e12, True)
diff --git a/scripts/base_train.py b/scripts/base_train.py
index ddd2c98..5d486d3 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -26,6 +26,7 @@ from nanochat.tokenizer import get_tokenizer, get_token_bytes
 from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.loss_eval import evaluate_bpb
 from nanochat.engine import Engine
+from nanochat.mfu import get_promised_flops_per_gpu
 from scripts.base_eval import evaluate_model
 print_banner()
 
@@ -138,6 +139,10 @@ print0(f"Total number of training tokens: {total_tokens:,}")
 print0(f"Tokens : Params ratio: {total_batch_size * num_iterations / num_params:.2f}") # Chinchilla is ~20
 print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens:e}")
 
+device_name, promised_flops_per_gpu, promised_is_estimated = get_promised_flops_per_gpu()
+promised_flops_per_sec = promised_flops_per_gpu * ddp_world_size
+print0(f"Detected GPU: {device_name} | peak BF16 TFLOPs (per GPU): {promised_flops_per_gpu / 1e12:.1f}{'*' if promised_is_estimated else ''}")
+
 # -----------------------------------------------------------------------------
 # Initialize the Optimizer (Muon for Linear layers, AdamW for embedding and lm_head)
 optimizers = model.setup_optimizers(unembedding_lr=unembedding_lr, embedding_lr=embedding_lr, matrix_lr=matrix_lr, weight_decay=weight_decay)
@@ -296,11 +301,10 @@ for step in range(num_iterations + 1):
     pct_done = 100 * step / num_iterations
     tok_per_sec = int(total_batch_size / dt)
     flops_per_sec = num_flops_per_token * total_batch_size / dt
-    promised_flops_per_sec_h100 = 989e12 * ddp_world_size # bfloat16 H100 SXM and without 2:4 sparsity
-    mfu = 100 * flops_per_sec / promised_flops_per_sec_h100 # in %
+    mfu = 100 * flops_per_sec / promised_flops_per_sec # in %
     if step > 10:
         total_training_time += dt # only count the time after the first 10 steps
-    print0(f"step {step:05d}/{num_iterations:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | total time: {total_training_time/60:.2f}m")
+    print0(f"step {step:05d}/{num_iterations:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f}{'*' if promised_is_estimated else ''} | total time: {total_training_time/60:.2f}m")
     if step % 100 == 0:
         wandb_run.log({
             "step": step,
@@ -337,7 +341,7 @@ get_report().log(section="Base model training", data=[
         "Minimum validation bpb": min_val_bpb,
         "Final validation bpb": val_bpb,
         "CORE metric estimate": results.get("core_metric", None),
-        "MFU %": f"{mfu:.2f}%",
+        "MFU %": f"{mfu:.2f}{'*' if promised_is_estimated else ''}%",
         "Total training flops": f"{flops_so_far:e}",
         "Total training time": f"{total_training_time/60:.2f}m",
         "Peak memory usage": f"{get_max_memory() / 1024 / 1024:.2f}MiB",
diff --git a/scripts/mid_train.py b/scripts/mid_train.py
index 6c2b82f..f4ae249 100644
--- a/scripts/mid_train.py
+++ b/scripts/mid_train.py
@@ -21,6 +21,7 @@ from nanochat.tokenizer import get_token_bytes
 from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.loss_eval import evaluate_bpb
 from nanochat.checkpoint_manager import load_model
+from nanochat.mfu import get_promised_flops_per_gpu
 import torch.distributed as dist
 
 from tasks.common import TaskMixture
@@ -81,6 +82,10 @@ grad_accum_steps = total_batch_size // world_tokens_per_fwdbwd
 print0(f"Tokens / micro-batch / rank: {device_batch_size} x {max_seq_len} = {tokens_per_fwdbwd:,}")
 print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
 print0(f"Total batch size {total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
+device_name, promised_flops_per_gpu, promised_is_estimated = get_promised_flops_per_gpu()
+promised_flops_per_sec = promised_flops_per_gpu * ddp_world_size
+tflo_ps = promised_flops_per_gpu / 1e12
+print0(f"Detected GPU: {device_name} | peak BF16 TFLOPs (per GPU): {tflo_ps:.1f}{'*' if promised_is_estimated else ''}")
 token_bytes = get_token_bytes(device=device)
 
 # Initialize the Optimizer (Muon for Linear layers, AdamW for embedding and lm_head)
@@ -270,11 +275,11 @@ while True:
     pct_done = 100 * progress
     tok_per_sec = int(total_batch_size / dt)
     flops_per_sec = num_flops_per_token * total_batch_size / dt
-    promised_flops_per_sec_h100 = 989e12 * ddp_world_size # bfloat16 H100 SXM and without 2:4 sparsity
-    mfu = 100 * flops_per_sec / promised_flops_per_sec_h100 # in %
+    mfu = 100 * flops_per_sec / promised_flops_per_sec # in %
     if step > 10:
         total_training_time += dt # only count the time after the first 10 steps
-    print0(f"step {step:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | total time: {total_training_time/60:.2f}m")
+    mfu_display = f"{mfu:.2f}{'*' if promised_is_estimated else ''}"
+    print0(f"step {step:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu_display} | total time: {total_training_time/60:.2f}m")
     if step % 10 == 0:
         wandb_run.log({
             "step": step,