diff --git a/test/external/external_test_speed_llama.py b/test/external/external_test_speed_llama.py
index 1ae0023ac5..92fd93f9cd 100644
--- a/test/external/external_test_speed_llama.py
+++ b/test/external/external_test_speed_llama.py
@@ -20,6 +20,7 @@ class TestLLaMASpeed(unittest.TestCase):
   def test_llama_compile(self):
     backup_program = Device[Device.DEFAULT].runtime
     backup_allocator = Device[Device.DEFAULT].allocator
+    backup_compiler = Device[Device.DEFAULT].compiler
     Device[Device.DEFAULT].runtime = FakeProgram
     Device[Device.DEFAULT].allocator = FakeAllocator()
 
@@ -33,20 +34,24 @@ class TestLLaMASpeed(unittest.TestCase):
     def run_llama(st, empty_method_cache=True):
       if empty_method_cache: Device[Device.DEFAULT].get_runner.cache_clear()
       tms = [time.perf_counter()]
-      for i in range(10):
+      for i in range(5):
         model(Tensor([[1,2,3,4]]), i).realize()
         tms.append(time.perf_counter())
       timings = [(tms[i+1]-tms[i])*1000 for i in range(len(tms)-1)]
       print(f"{st:15s} mean runtime: {sum(timings)/len(timings):7.2f}ms, runs: ", ", ".join(f'{x:7.2f}' for x in timings))
 
-    run_llama("codegen")
-    run_llama("methodcache", False)
+    run_llama("codegen(0)")
+    run_llama("codegen(1)")
 
-    with Profiling(sort='time', frac=0.1):
-      run_llama("profile")
+    # test no compiler use for this
+    Device[Device.DEFAULT].compiler = None
+    run_llama("methodcache", False)
+    with Profiling(sort='time', frac=0.1, fn="/tmp/llama.prof"):
+      run_llama("profile", False)
 
     Device[Device.DEFAULT].runtime = backup_program
     Device[Device.DEFAULT].allocator = backup_allocator
+    Device[Device.DEFAULT].compiler = backup_compiler
 
 if __name__ == '__main__':
   unittest.main()
diff --git a/test/test_method_cache.py b/test/test_method_cache.py
new file mode 100644
index 0000000000..0ab1c88c84
--- /dev/null
+++ b/test/test_method_cache.py
@@ -0,0 +1,49 @@
+import unittest
+from tinygrad import Tensor, Device, Variable
+from tinygrad.device import Compiled
+from examples.gpt2 import Transformer
+from tinygrad.nn.state import get_state_dict
+
+@unittest.skipIf(not isinstance(Device[Device.DEFAULT], Compiled), "only test for compiled backends")
+class TestMethodCache(unittest.TestCase):
+  def setUp(self):
+    self.backup_compiler = Device[Device.DEFAULT].compiler
+  def tearDown(self):
+    Device[Device.DEFAULT].compiler = self.backup_compiler
+
+  def test_simple_methodcache(self):
+    a = Tensor([1])
+    b = Tensor([2])
+    c = Tensor([3])
+    d = Tensor([4])
+    (a+b).realize()
+    Device[Device.DEFAULT].compiler = None
+    (c+d).realize()
+
+  def test_nested_methodcache(self):
+    a,b,c,d = Tensor([1]), Tensor([2]), Tensor([3]), Tensor([4])
+    ((a+b)+(a+b)).realize()
+    Device[Device.DEFAULT].compiler = None
+    ((c+d)+(c+d)).realize()
+
+  def test_nested_methodcache_swap(self):
+    a,b,c,d = Tensor([1]), Tensor([2]), Tensor([3]), Tensor([4])
+    ((a+b)+(c+d)).realize()
+    Device[Device.DEFAULT].compiler = None
+    ((c+d)+(a+b)).realize()
+
+  def test_small_transformer(self):
+    args_tiny = {"dim": 16, "n_heads": 8, "n_layers": 8, "norm_eps": 1e-05, "vocab_size": 10}
+    model = Transformer(**args_tiny)
+    for v in get_state_dict(model).values(): v.assign(Tensor.empty(*v.shape, dtype=v.dtype).realize())
+    # NOTE: you have to do this twice due to the k-v cache
+    for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
+    for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
+    Device[Device.DEFAULT].compiler = None
+    for i in range(3): model(Tensor([[1,2,3,4]]), Variable("start_pos", 0, 10).bind(i)).realize()
+
+if __name__ == '__main__':
+  unittest.main()
+
+
+
diff --git a/tinygrad/device.py b/tinygrad/device.py
index d6a32b8884..47770719a2 100644
--- a/tinygrad/device.py
+++ b/tinygrad/device.py
@@ -285,6 +285,7 @@ class Compiled:
   def synchronize(self): pass  # override this in your device
 
   def to_program(self, k:Linearizer) -> CompiledASTRunner:
+    assert self.compiler is not None, f"compiler is None, can't build {k.ast}"
     k.linearize()
     src = self.renderer(to_function_name(k.name), k.uops)
     if getenv("DISABLE_COMPILER_CACHE") or '<' in self.compiler.__name__:
diff --git a/tinygrad/helpers.py b/tinygrad/helpers.py
index 4b2090296c..5cfd82ffcc 100644
--- a/tinygrad/helpers.py
+++ b/tinygrad/helpers.py
@@ -91,13 +91,14 @@ class Timing(contextlib.ContextDecorator):
     if self.enabled: print(f"{self.prefix}{self.et*1e-6:6.2f} ms"+(self.on_exit(self.et) if self.on_exit else ""))
 
 class Profiling(contextlib.ContextDecorator):
-  def __init__(self, enabled=True, sort='cumtime', frac=0.2): self.enabled, self.sort, self.frac = enabled, sort, frac
+  def __init__(self, enabled=True, sort='cumtime', frac=0.2, fn=None): self.enabled, self.sort, self.frac, self.fn = enabled, sort, frac, fn
   def __enter__(self):
     self.pr = cProfile.Profile(timer=lambda: int(time.time()*1e9), timeunit=1e-6)
     if self.enabled: self.pr.enable()
   def __exit__(self, *exc):
     if self.enabled:
       self.pr.disable()
+      if self.fn: self.pr.dump_stats(self.fn)
       pstats.Stats(self.pr).strip_dirs().sort_stats(self.sort).print_stats(self.frac)
 
 # *** universal database cache ***
diff --git a/tinygrad/shape/shapetracker.py b/tinygrad/shape/shapetracker.py
index 7600307cfc..43d34fac23 100644
--- a/tinygrad/shape/shapetracker.py
+++ b/tinygrad/shape/shapetracker.py
@@ -75,6 +75,7 @@ class ShapeTracker:
   @property
   def shape(self) -> Tuple[sint, ...]: return self.views[-1].shape
 
+  @functools.lru_cache(maxsize=None)   # NOTE: this keeps all ShapeTrackers alive
   def size(self) -> int:
     if 0 in self.shape: return 0
     ret = self.expr_idxs()[0].max