diff --git a/nanochat/common.py b/nanochat/common.py
index 22559ce..faf9144 100644
--- a/nanochat/common.py
+++ b/nanochat/common.py
@@ -200,3 +200,52 @@ class DummyWandb:
         pass
     def finish(self):
         pass
+
+# hardcoded BF16 peak flops for NVIDIA A100, H100, H200, B200 GPU and AMD MI250, MI300X, MI325X, MI355X and Intel PVC
+# inspired by torchtitan: https://github.com/pytorch/torchtitan/blob/main/torchtitan/tools/utils.py
+def get_peak_flops(device_name: str) -> float:
+    if "A100" in device_name:
+        # data from https://www.nvidia.com/en-us/data-center/a100/
+        return 312e12
+    elif "H100" in device_name:
+        # data from https://www.nvidia.com/en-us/data-center/h100/
+        # NOTE: Specifications are one-half lower without sparsity.
+        if "NVL" in device_name:
+            return 835e12
+        elif "PCIe" in device_name:
+            return 756e12
+        else:  # for H100 SXM and other variants
+            return 989e12
+    elif "H200" in device_name:
+        # data from https://www.nvidia.com/en-us/data-center/h200/
+        return 989e12
+    elif "B200" in device_name:
+        # data from https://nvdam.widen.net/s/wwnsxrhm2w/blackwell-datasheet-3384703
+        return 2.25e15
+    elif "MI355X" in device_name:
+        # MI355X data from https://www.amd.com/en/products/accelerators/instinct/mi350/mi355x.html
+        return 2500e12
+    elif "MI300X" in device_name or "MI325X" in device_name:
+        # MI300X data from https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html
+        # MI325X data from https://www.amd.com/en/products/accelerators/instinct/mi300/mi325x.html
+        return 1300e12
+    elif "MI250X" in device_name:
+        # data from https://www.amd.com/en/products/accelerators/instinct/mi200/mi250x.html (per GCD)
+        return 191.5e12
+    elif "Data Center GPU Max 1550" in device_name:
+        # Also known as Ponte Vecchio (PVC).
+        # data from https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2025-0/intel-xe-gpu-architecture.html
+        # Dot Product Accumulate Systolic (DPAS):
+        # - Freq: 1300MHz
+        # - #ops: 512
+        # Full EU mode (i.e. 512 max compute units): 340.8 TFLOPS (BF16)
+        # Standard EU mode (i.e. 448 max compute units): 298.2 TFLOPS (BF16)
+        max_comp_units = torch.xpu.get_device_properties("xpu").max_compute_units
+        return 512 * max_comp_units * 1300 * 10**6
+    elif "l40s" in device_name:
+        # data from: "https://resources.nvidia.com/en-us-l40s/l40s-datasheet-28413"
+        return 362e12
+
+    else:  # for other GPU types, assume A100
+        logger.warning(f"Peak flops undefined for: {device_name}, fallback to A100")
+        return 312e12
diff --git a/scripts/base_train.py b/scripts/base_train.py
index c61986e..e051f99 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -22,7 +22,7 @@ import torch
 
 from nanochat.gpt import GPT, GPTConfig
 from nanochat.dataloader import tokenizing_distributed_data_loader_bos_bestfit, tokenizing_distributed_data_loader_with_state_bos_bestfit
-from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir, autodetect_device_type
+from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, print_banner, get_base_dir, autodetect_device_type, get_peak_flops
 from nanochat.tokenizer import get_tokenizer, get_token_bytes
 from nanochat.checkpoint_manager import save_checkpoint, load_checkpoint
 from nanochat.loss_eval import evaluate_bpb
@@ -82,6 +82,12 @@ master_process = ddp_rank == 0 # this process will do logging, checkpointing etc
 autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=torch.bfloat16) if device_type == "cuda" else nullcontext()
 synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
 get_max_memory = torch.cuda.max_memory_allocated if device_type == "cuda" else lambda: 0
+if device_type == "cuda":
+    gpu_device_name = torch.cuda.get_device_name(0)
+    gpu_peak_flops = get_peak_flops(gpu_device_name)
+    print0(f"GPU: {gpu_device_name} | Peak FLOPS (BF16): {gpu_peak_flops:.2e}")
+else:
+    gpu_peak_flops = float('inf')  # MFU not meaningful for CPU/MPS
 
 # wandb logging init
 use_dummy_wandb = args.run == "dummy" or not master_process
@@ -395,8 +401,7 @@ while True:
     pct_done = 100 * step / num_iterations
     tok_per_sec = int(args.total_batch_size / dt)
     flops_per_sec = num_flops_per_token * args.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 / (gpu_peak_flops * ddp_world_size)
     if step > 10:
         total_training_time += dt # only count the time after the first 10 steps
     # Calculate ETA based on average time per step (excluding first 10 steps)