diff --git a/examples/hlb_cifar10.py b/examples/hlb_cifar10.py
index ebbd70dd30..8cd6548dcc 100644
--- a/examples/hlb_cifar10.py
+++ b/examples/hlb_cifar10.py
@@ -83,6 +83,17 @@ def train_cifar():
   print(X_train.shape, Y_train.shape)
   Xt, Yt = fetch_batch(X_test, Y_test, BS=BS)
   model = SpeedyResNet()
+
+  # init weights with torch
+  if getenv("TORCHWEIGHTS"):
+    from examples.hlb_cifar10_torch import SpeedyResNet as SpeedyResNetTorch
+    torch_model = SpeedyResNetTorch()
+    model_state_dict = optim.get_state_dict(model)
+    torch_state_dict = torch_model.state_dict()
+    for k,v in torch_state_dict.items():
+      print(f"initting {k} from torch")
+      model_state_dict[k].assign(Tensor(v.detach().numpy())).realize()
+
   if getenv("ADAM"):
     optimizer = optim.Adam(optim.get_parameters(model), lr=Tensor([0.001]).realize())
   else:
diff --git a/examples/hlb_cifar10_torch.py b/examples/hlb_cifar10_torch.py
index 73fca1e51a..9a2a9afafd 100644
--- a/examples/hlb_cifar10_torch.py
+++ b/examples/hlb_cifar10_torch.py
@@ -30,28 +30,28 @@ class ConvGroup(nn.Module):
     x = self.norm[2](self.conv[2](x) * mult).relu()
     return x + residual
 
+class GlobalMaxPool(nn.Module):
+  def forward(self, x): return torch.amax(x, dim=(2,3))
+
 class SpeedyResNet(nn.Module):
   def __init__(self):
     super().__init__()
     # TODO: add whitening
-    self.ic = nn.Conv2d(3, 64, kernel_size=1)
-    self.ib = nn.BatchNorm2d(64, track_running_stats=False, eps=1e-12, momentum=0.8)
     self.net = nn.ModuleList([
+      nn.Conv2d(3, 64, kernel_size=1),
+      nn.BatchNorm2d(64, track_running_stats=False, eps=1e-12, momentum=0.8),
+      nn.ReLU(),
       ConvGroup(64, 128, short=False),
       ConvGroup(128, 256, short=True),
       ConvGroup(256, 512, short=False),
+      GlobalMaxPool(),
+      nn.Linear(512, num_classes, bias=False)
     ])
-    self.lin = nn.Linear(512, num_classes, bias=False)
 
   # note, pytorch just uses https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html instead of log_softmax
   def forward(self, x):
-    x = self.ic(x)
-    x = self.ib(x)
-    x = x.relu()
     for layer in self.net:
       x = layer(x)
-    x = torch.amax(x, dim=(2,3))
-    x = self.lin(x)
     return x.log_softmax(-1)
 
 def train_step_jitted(model, optimizer, X, Y):
diff --git a/tinygrad/runtime/ops_hip.py b/tinygrad/runtime/ops_hip.py
new file mode 100644
index 0000000000..6163e9a2a0
--- /dev/null
+++ b/tinygrad/runtime/ops_hip.py
@@ -0,0 +1,57 @@
+import numpy as np
+import ctypes
+from pyhip import hip, hiprtc # type: ignore
+from tinygrad.helpers import DEBUG
+from tinygrad.ops import Compiled
+from tinygrad.runtime.lib import RawBufferCopyInOut
+from tinygrad.codegen.cstyle import CStyleCodegen, CStyleLanguage
+
+# The default HIP stream is used for everything.
+
+class RawHIPBuffer(RawBufferCopyInOut):
+  def __init__(self, size, dtype):
+    self.buf_sz = size * dtype.itemsize
+    super().__init__(size, dtype, hip.hipMalloc(self.buf_sz))
+  def _copyin(self, x:np.ndarray): hip.hipMemcpyAsync_htod(self._buf, x.ctypes.data, self.buf_sz, 0)
+  def _copyout(self, x:np.ndarray): hip.hipMemcpyAsync_dtoh(x.ctypes.data, self._buf, self.buf_sz, 0)
+
+class HIPProgram:
+  def __init__(self, name:str, prg:str, binary=False):
+    try:
+      if not binary:
+        prog = hiprtc.hiprtcCreateProgram(prg, name, [], [])
+        device_properties = hip.hipGetDeviceProperties(0)
+        hiprtc.hiprtcCompileProgram(prog, [f'--offload-arch={device_properties.gcnArchName}'])
+        prg = hiprtc.hiprtcGetCode(prog)
+    except hip.hipError as e:
+      if DEBUG >= 3: print("FAILED TO BUILD", prg)
+      raise e
+    if DEBUG >= 5: print(prg)
+    module = hip.hipModuleLoadData(prg)
+    self.prg = hip.hipModuleGetFunction(module, name)
+
+  def __call__(self, global_size, local_size, *args, wait=False):
+    local_size = (local_size + [1] * (3 - len(local_size))) if local_size is not None else (1,1,1)
+    global_size = global_size + [1] * (3 - len(global_size))
+    assert all(x%y == 0 for x,y in zip(global_size, local_size)), f"local:{local_size} must divide global:{global_size}"
+    global_size = [x//y for x,y in zip(global_size, local_size)]
+    if wait:
+      start, end = hip.hipEventCreate(), hip.hipEventCreate()
+      hip.hipEventRecord(start)
+    class PackageStruct(ctypes.Structure):
+      _fields_ = [(f'field{idx}', ctypes.c_void_p) for idx in range(len(args))]
+    struct = PackageStruct(*[data._buf for data in args])
+    hip.hipModuleLaunchKernel(self.prg, global_size[0], global_size[1], global_size[2], local_size[0], local_size[1], local_size[2], 0, 0, struct)
+    if wait:
+      hip.hipEventRecord(end)
+      hip.hipEventSynchronize(end)
+      return hip.hipEventElapsedTime(start, end)*1e-3
+
+class HIPCodegen(CStyleCodegen):
+  lang = CStyleLanguage(
+    kernel_prefix = "#define INFINITY (__builtin_inff())\nextern \"C\" __global__", smem_prefix = "__shared__ ", barrier = "__syncthreads();", float4 = "make_float4",
+    half_prekernel = "",
+    gid = [f'blockDim.{chr(120+i)}*blockIdx.{chr(120+i)}+threadIdx.{chr(120+i)}' for i in range(3)],
+    lid = [f'threadIdx.{chr(120+i)}' for i in range(3)])
+
+HIPBuffer = Compiled(RawHIPBuffer, HIPCodegen, HIPProgram, hip.hipDeviceSynchronize)