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diff --git a/bitsandbytes/optim/lars.py b/bitsandbytes/optim/lars.py
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+# Copyright (c) Facebook, Inc. and its affiliates. 
+#   
+# This source code is licensed under the MIT license found in the 
+# LICENSE file in the root directory of this source tree.
+import torch
+
+from torch.optim import Optimizer
+from bitsandbytes.optim.optimizer import Optimizer1State
+
+class LARS(Optimizer1State):
+    def __init__(self, params, lr, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False, optim_bits=32, args=None,
+                 min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
+        if momentum == 0:
+            raise NotImplementError(f'LARS without momentum is not supported!')
+        super(LARS, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
+                weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
+
+class LARS8bit(Optimizer1State):
+    def __init__(self, params, lr, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False, args=None,
+                 min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
+        if momentum == 0:
+            raise NotImplementError(f'LARS without momentum is not supported!')
+        super(LARS8bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
+                weight_decay, 8, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
+
+class LARS32bit(Optimizer1State):
+    def __init__(self, params, lr, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False, args=None,
+                 min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
+        if momentum == 0:
+            raise NotImplementError(f'LARS without momentum is not supported!')
+        super(LARS32bit, self).__init__('lars', params, lr, (momentum, dampening), 0.0,
+                weight_decay, 32, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
+
+
+class PytorchLARS(Optimizer):
+    def __init__(self, params, lr=0.01, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False, max_unorm=0.02):
+        if lr < 0.0:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if momentum < 0.0:
+            raise ValueError("Invalid momentum value: {}".format(momentum))
+        if weight_decay < 0.0:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
+                        weight_decay=weight_decay, nesterov=nesterov, max_unorm=max_unorm)
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+        super(PytorchLARS, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(PytorchLARS, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('nesterov', False)
+
+    @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.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            params_with_grad = []
+            d_p_list = []
+            momentum_buffer_list = []
+            weight_decay = group['weight_decay']
+            momentum = group['momentum']
+            dampening = group['dampening']
+            nesterov = group['nesterov']
+            max_unorm = group['max_unorm']
+            lr = group['lr']
+
+            for p in group['params']:
+                if p.grad is None: continue
+
+                state = self.state[p]
+                d_p = p.grad
+                if weight_decay != 0:
+                    d_p = d_p.add(param, alpha=weight_decay)
+
+                if momentum != 0:
+                    buf = state.get('momentum_buffer', None)
+
+                    if buf is None:
+                        buf = torch.clone(d_p).detach()
+                        state['momentum_buffer']= buf
+                    else:
+                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
+
+                    if nesterov:
+                        update = d_p + buf*momentum
+                    else:
+                        update = buf
+
+                update_scale = 1.0
+                if max_unorm > 0.0:
+                    assert p.dtype == torch.float32
+                    pnorm = torch.norm(p.detach())
+                    unorm = torch.norm(update)
+                    if unorm > max_unorm*pnorm:
+                        update_scale = max_unorm*pnorm/unorm
+
+                p.add_(update, alpha=-lr*update_scale)
+
+        return loss