diff --git a/timm/optim/nadamw.py b/timm/optim/nadamw.py
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+""" NAdamW Optimizer
+
+Based on simplified algorithm in https://github.com/mlcommons/algorithmic-efficiency/tree/main/baselines/nadamw
+
+Added multi-tensor (foreach) path.
+"""
+import math
+from typing import List, Optional
+
+import torch
+from torch import Tensor
+
+
+# Modified from github.com/pytorch/pytorch/blob/v1.12.1/torch/optim/adamw.py.
+class NAdamW(torch.optim.Optimizer):
+    r"""Implements NAdamW algorithm.
+
+      See Table 1 in https://arxiv.org/abs/1910.05446 for the implementation of
+      the NAdam algorithm (there is also a comment in the code which highlights
+      the only difference of NAdamW and AdamW).
+      For further details regarding the algorithm we refer to
+      `Decoupled Weight Decay Regularization`_.
+
+      Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay coefficient (default: 1e-2)
+      .. _Decoupled Weight Decay Regularization:
+          https://arxiv.org/abs/1711.05101
+      .. _On the Convergence of Adam and Beyond:
+          https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(
+            self,
+            params,
+            lr=1e-3,
+            betas=(0.9, 0.999),
+            eps=1e-8,
+            weight_decay=1e-2,
+            maximize: bool = False,
+            foreach: Optional[bool] = None,
+            capturable: bool = False,
+    ):
+        if not 0.0 <= lr:
+            raise ValueError(f'Invalid learning rate: {lr}')
+        if not 0.0 <= eps:
+            raise ValueError(f'Invalid epsilon value: {eps}')
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f'Invalid beta parameter at index 0: {betas[0]}')
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f'Invalid beta parameter at index 1: {betas[1]}')
+        if not 0.0 <= weight_decay:
+            raise ValueError(f'Invalid weight_decay value: {weight_decay}')
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            foreach=foreach,
+            maximize=maximize,
+            capturable=capturable,
+        )
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        state_values = list(self.state.values())
+        step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
+            state_values[0]['step'])
+        if not step_is_tensor:
+            for s in state_values:
+                s['step'] = torch.tensor(float(s['step']))
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+            Args:
+              closure (callable, optional): A closure that reevaluates the model
+                  and returns the loss.
+        """
+        self._cuda_graph_capture_health_check()
+
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad = []
+            grads = []
+            exp_avgs = []
+            exp_avg_sqs = []
+            state_steps = []
+            beta1, beta2 = group['betas']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError('NAdamW does not support sparse gradients')
+                grads.append(p.grad)
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = torch.tensor(0.)
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+
+                exp_avgs.append(state['exp_avg'])
+                exp_avg_sqs.append(state['exp_avg_sq'])
+                state_steps.append(state['step'])
+
+            nadamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                state_steps,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group['lr'],
+                weight_decay=group['weight_decay'],
+                eps=group['eps'],
+                maximize=group['maximize'],
+                capturable=group['capturable'],
+            )
+
+        return loss
+
+
+def nadamw(
+        params: List[Tensor],
+        grads: List[Tensor],
+        exp_avgs: List[Tensor],
+        exp_avg_sqs: List[Tensor],
+        state_steps: List[Tensor],
+        foreach: Optional[bool] = None,
+        capturable: bool = False,
+        *,
+        beta1: float,
+        beta2: float,
+        lr: float,
+        weight_decay: float,
+        eps: float,
+        maximize: bool,
+) -> None:
+    r"""Functional API that performs NAdamW algorithm computation.
+      See NAdamW class for details.
+    """
+
+    if not all(isinstance(t, torch.Tensor) for t in state_steps):
+        raise RuntimeError(
+            'API has changed, `state_steps` argument must contain a list of' +
+            ' singleton tensors')
+
+    if foreach is None:
+        foreach = True
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_nadamw
+    else:
+        func = _single_tensor_nadamw
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        exp_avg_sqs,
+        state_steps,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        eps=eps,
+        maximize=maximize,
+        capturable=capturable,
+    )
+
+
+def _single_tensor_nadamw(
+        params: List[Tensor],
+        grads: List[Tensor],
+        exp_avgs: List[Tensor],
+        exp_avg_sqs: List[Tensor],
+        state_steps: List[Tensor],
+        *,
+        beta1: float,
+        beta2: float,
+        lr: float,
+        weight_decay: float,
+        eps: float,
+        maximize: bool,
+        capturable: bool
+):
+
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+        exp_avg_sq = exp_avg_sqs[i]
+        step_t = state_steps[i]
+
+        # Update step.
+        step_t += 1
+
+        # Perform stepweight decay.
+        param.mul_(1. - lr * weight_decay)
+
+        # Decay the first and second moment running average coefficient.
+        exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+        if capturable:
+            step = step_t
+
+            # 1 - beta1 ** step can't be captured in a CUDA graph, even if step is a CUDA tensor
+            # (incurs "RuntimeError: CUDA error: operation not permitted when stream is capturing")
+            bias_correction1 = 1 - torch.pow(beta1, step)
+            bias_correction2 = 1 - torch.pow(beta2, step)
+
+            step_size = lr / bias_correction1
+            step_size_neg = step_size.neg()
+
+            bias_correction2_sqrt = bias_correction2.sqrt()
+
+            # Only difference between NAdamW and AdamW in this implementation.
+            # The official PyTorch implementation of NAdam uses a different algorithm.
+            exp_avg = exp_avg.mul(beta1).add_(grad, alpha=1 - beta1)
+
+            denom = (exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)).add_(eps / step_size_neg)
+            param.addcdiv_(exp_avg, denom)
+        else:
+            step = step_t.item()
+            bias_correction1 = 1 - beta1 ** step
+            bias_correction2 = 1 - beta2 ** step
+            step_size = lr / bias_correction1
+            bias_correction2_sqrt = math.sqrt(bias_correction2)
+
+            # Only difference between NAdamW and AdamW in this implementation.
+            # The official PyTorch implementation of NAdam uses a different algorithm.
+            exp_avg = exp_avg.mul(beta1).add_(grad, alpha=1 - beta1)
+
+            denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps)
+            param.addcdiv_(exp_avg, denom, value=-step_size)
+
+
+def _multi_tensor_nadamw(
+        params: List[Tensor],
+        grads: List[Tensor],
+        exp_avgs: List[Tensor],
+        exp_avg_sqs: List[Tensor],
+        state_steps: List[Tensor],
+        *,
+        beta1: float,
+        beta2: float,
+        lr: float,
+        weight_decay: float,
+        eps: float,
+        maximize: bool,
+        capturable: bool,
+):
+    if len(params) == 0:
+        return
+
+    if capturable:
+        assert all(
+            p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)
+        ), "If capturable=True, params and state_steps must be CUDA tensors."
+
+    if maximize:
+        grads = torch._foreach_neg(tuple(grads))  # type: ignore[assignment]
+
+    grads = [torch.view_as_real(x) if torch.is_complex(x) else x for x in grads]
+    exp_avgs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avgs]
+    exp_avg_sqs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avg_sqs]
+    params = [torch.view_as_real(x) if torch.is_complex(x) else x for x in params]
+
+    # update steps
+    torch._foreach_add_(state_steps, 1)
+
+    # Perform stepweight decay
+    torch._foreach_mul_(params, 1 - lr * weight_decay)
+
+    # Decay the first and second moment running average coefficient
+    torch._foreach_mul_(exp_avgs, beta1)
+    torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
+
+    torch._foreach_mul_(exp_avg_sqs, beta2)
+    torch._foreach_addcmul_(exp_avg_sqs, grads, grads, 1 - beta2)
+
+    if capturable:
+        # TODO: use foreach_pow if/when foreach_pow is added
+        bias_correction1 = [torch.pow(beta1, step) for step in state_steps]
+        bias_correction2 = [torch.pow(beta2, step) for step in state_steps]
+        # foreach_sub doesn't allow a scalar as the first arg
+        torch._foreach_sub_(bias_correction1, 1)
+        torch._foreach_sub_(bias_correction2, 1)
+        torch._foreach_neg_(bias_correction1)
+        torch._foreach_neg_(bias_correction2)
+
+        # foreach_div doesn't allow a scalar as the first arg
+        step_size = torch._foreach_div(bias_correction1, lr)
+        torch._foreach_reciprocal_(step_size)
+        torch._foreach_neg_(step_size)
+
+        bias_correction2_sqrt = torch._foreach_sqrt(bias_correction2)
+
+        exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
+        torch._foreach_div_(
+            exp_avg_sq_sqrt, torch._foreach_mul(bias_correction2_sqrt, step_size)
+        )
+        eps_over_step_size = torch._foreach_div(step_size, eps)
+        torch._foreach_reciprocal_(eps_over_step_size)
+        denom = torch._foreach_add(exp_avg_sq_sqrt, eps_over_step_size)
+
+        torch._foreach_addcdiv_(params, exp_avgs, denom)
+    else:
+        bias_correction1 = [1 - beta1 ** step.item() for step in state_steps]
+        bias_correction2 = [1 - beta2 ** step.item() for step in state_steps]
+
+        step_size = [(lr / bc) * -1 for bc in bias_correction1]
+
+        bias_correction2_sqrt = [math.sqrt(bc) for bc in bias_correction2]
+
+        # Only difference between NAdamW and AdamW in this implementation.
+        # The official PyTorch implementation of NAdam uses a different algorithm.
+        exp_avgs = torch._foreach_mul(exp_avgs, beta1)
+        torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
+
+        exp_avg_sq_sqrt = torch._foreach_sqrt(exp_avg_sqs)
+        torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt)
+        denom = torch._foreach_add(exp_avg_sq_sqrt, eps)
+
+        torch._foreach_addcdiv_(params, exp_avgs, denom, step_size)