diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index ab604c6646..1d1156c847 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -30,6 +30,10 @@ jobs:
         ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
         python3 examples/llama.py --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
         JIT=1 python3 examples/llama.py --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+    - name: Run GPT2
+      run: |
+        python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
+        JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
     - name: Run 10 CIFAR training steps
       run: |
         ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
@@ -43,6 +47,8 @@ jobs:
           train_cifar.txt
           llama_unjitted.txt
           llama_jitted.txt
+          gpt2_unjitted.txt
+          gpt2_jitted.txt
 
   testamdbenchmark:
     name: AMD Benchmark
@@ -67,6 +73,10 @@ jobs:
         ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
         python3 examples/llama.py --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
         JIT=1 python3 examples/llama.py --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_jitted.txt
+    - name: Run GPT2
+      run: |
+        python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
+        JIT=1 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
     - name: Run 10 CIFAR training steps
       run: |
         ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
@@ -80,3 +90,5 @@ jobs:
           train_cifar.txt
           llama_unjitted.txt
           llama_jitted.txt
+          gpt2_unjitted.txt
+          gpt2_jitted.txt
diff --git a/examples/gpt2.py b/examples/gpt2.py
new file mode 100644
index 0000000000..d6b763da7c
--- /dev/null
+++ b/examples/gpt2.py
@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+# pip3 install tiktoken
+
+import functools, argparse
+import numpy as np
+from tqdm import trange
+np.set_printoptions(linewidth=200)
+from typing import Optional, Tuple
+
+from tinygrad.helpers import Timing, getenv, dtypes, DEBUG
+from tinygrad.ops import GlobalCounters
+from tinygrad.lazy import Device
+from tinygrad.tensor import Tensor
+from tinygrad.nn import Embedding, Linear
+from tinygrad.jit import TinyJit
+
+from examples.llama import sample
+
+class LayerNorm:
+  def __init__(self, dim, eps=1e-5):
+    self.eps = eps
+    self.weight = Tensor.ones(dim)
+    self.bias = Tensor.zeros(dim)
+
+  def __call__(self, x:Tensor):
+    return (x.layernorm(eps=self.eps)) * self.weight + self.bias
+
+class Attention:
+  def __init__(self, dim, n_heads, linear=Linear):
+    self.c_attn = linear(dim, 3*dim, bias=True)
+    self.c_proj = linear(dim, dim, bias=True)
+    self.n_heads = n_heads
+    self.dim = dim
+    self.head_dim = dim // n_heads
+
+  def prepare_attention(self, x:Tensor) -> Tuple[Tensor, Tensor, Tensor]:
+    xqkv = self.c_attn(x)
+    xq, xk, xv = [xqkv.slice([None, None, (i*self.dim, (i+1)*self.dim)]) for i in range(3)]
+    xq, xk, xv = [x.reshape(x.shape[0], x.shape[1], self.n_heads, self.head_dim) for x in (xq, xk, xv)]
+    return xq, xk, xv
+
+  def inner_attention(self, xq:Tensor, xk:Tensor, xv:Tensor, start_pos:int, mask:Optional[Tensor]) -> Tensor:
+    bsz, seqlen, _, _ = xq.shape
+    # kv caching!
+    if start_pos == 0:
+      keys, values = xk, xv
+    else:
+      assert hasattr(self, 'cache_k'), "no cache"
+      assert start_pos == self.cache_k.shape[1] and start_pos == self.cache_v.shape[1], "cache is wrong shape"
+      assert seqlen == xk.shape[1] and seqlen == xv.shape[1], "seqlen is wrong shape?!?"
+      keys, values = self.cache_k.cat(xk, dim=1), self.cache_v.cat(xv, dim=1)
+
+    # save the cache
+    self.cache_k, self.cache_v = keys.realize(), values.realize()
+    xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)
+    return xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2).reshape(bsz, seqlen, -1)
+
+  # NOTE: this is not called
+  def __call__(self, x:Tensor, start_pos:int, mask:Optional[Tensor]) -> Tensor:
+    xq, xk, xv = self.prepare_attention(x)
+    output = self.inner_attention(xq, xk, xv, start_pos, mask)
+    return self.c_proj(output)
+
+class FeedForward:
+  def __init__(self, dim, hidden_dim, linear=Linear):
+    self.c_fc = linear(dim, hidden_dim, bias=True)
+    self.c_proj = linear(hidden_dim, dim, bias=True)
+
+  def __call__(self, x:Tensor) -> Tensor:
+    return self.c_proj(self.c_fc(x).gelu())
+
+class TransformerBlock:
+  def __init__(self, dim, n_heads, norm_eps, linear=Linear):
+    self.attn = Attention(dim, n_heads, linear)
+    self.mlp = FeedForward(dim, 4*dim, linear)
+    self.ln_1 = LayerNorm(dim, norm_eps)
+    self.ln_2 = LayerNorm(dim, norm_eps)
+    if getenv("JIT"):
+      self._pre = TinyJit(self.pre)
+      self._post = TinyJit(self.post)
+    else:
+      self._pre, self._post = self.pre, self.post
+
+  def pre(self, x:Tensor) -> Tuple[Tensor, Tensor, Tensor]:
+    xq, xk, xv = self.attn.prepare_attention(self.ln_1(x))
+    return xq.realize(), xk.realize(), xv.realize()
+
+  def post(self, x:Tensor, output:Tensor) -> Tensor:
+    h = x + self.attn.c_proj(output)
+    return (h + self.mlp(self.ln_2(h))).realize()
+
+  def __call__(self, x:Tensor, start_pos:int, mask:Optional[Tensor]):
+    xq, xk, xv = self._pre(x) if mask is None else self.pre(x)
+    # inner_attention can't be jitted because it's dynamic based on start_pos
+    output = self.attn.inner_attention(xq, xk, xv, start_pos, mask)
+    return self._post(x, output) if mask is None else self.post(x, output)
+
+class Transformer:
+  def __init__(self, dim, n_heads, n_layers, norm_eps=1e-5, vocab_size=50257, linear=Linear, max_seq_len=1024):
+    self.wte = Embedding(vocab_size, dim)
+    self.wpe = Embedding(max_seq_len, dim)
+    self.h = [TransformerBlock(dim, n_heads, norm_eps, linear) for _ in range(n_layers)]
+    self.ln_f = LayerNorm(dim, norm_eps)
+    self.lm_head = linear(dim, vocab_size, bias=False)
+
+  def __call__(self, tokens:Tensor, start_pos:int):
+    _bsz, seqlen = tokens.shape
+    tok_emb = self.wte(tokens)
+    pos = Tensor.arange(start_pos, start_pos + seqlen).reshape(1, -1)
+    pos_emb = self.wpe(pos)
+    h = tok_emb + pos_emb
+
+    # get only the part we are using. making it contiguous avoids more kernel calls
+    mask = Tensor.full((1, 1, seqlen, start_pos + seqlen), float("-inf"), dtype=dtypes.float32).triu(start_pos+1).realize() if seqlen > 1 else None
+    h = h.sequential([functools.partial(layer, start_pos=start_pos, mask=mask) for layer in self.h])
+    h = self.ln_f(h)
+    return self.lm_head(h)
+
+# **** files and arguments ****
+
+MODEL_PARAMS = {
+  'gpt2':         dict(n_layers=12, n_heads=12, dim=768),   # 124M params
+  'gpt2-medium':  dict(n_layers=24, n_heads=16, dim=1024),  # 350M params
+  'gpt2-large':   dict(n_layers=36, n_heads=20, dim=1280),  # 774M params
+  'gpt2-xl':      dict(n_layers=48, n_heads=25, dim=1600),  # 1558M params
+}
+
+def get_url(model_size): return f'https://huggingface.co/{model_size}/resolve/main/pytorch_model.bin'
+
+class GPT2:
+  @staticmethod
+  def build(model_size="gpt2"):
+    import tiktoken
+    from tinygrad.state import torch_load, load_state_dict
+    from extra.utils import fetch_as_file
+    tokenizer = tiktoken.get_encoding("gpt2")
+
+    params = MODEL_PARAMS[model_size]
+    model = Transformer(**params)
+    weights = torch_load(fetch_as_file(get_url(model_size)))
+    # special treatment for the Conv1D weights we need to transpose
+    transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
+    for k in weights.keys():
+      if any(k.endswith(w) for w in transposed):
+        weights[k] = Tensor(weights[k].numpy().T)
+    # lm head and wte are tied
+    weights['lm_head.weight'] = Tensor(weights['wte.weight'].numpy())
+
+    load_state_dict(model, weights)
+    return GPT2(model, tokenizer)
+
+  def __init__(self, model, tokenizer):
+    self.model = model
+    self.tokenizer = tokenizer
+
+  def greedy_until(self, prompt:str, max_length:int, temperature:float, timing:bool=False):
+    toks = self.tokenizer.encode(prompt, allowed_special={"<|endoftext|>"})
+    start_pos = 0
+    for _ in trange(max_length, disable=(timing==True)):
+      if args.timing: print("")
+      st = GlobalCounters.time_sum_s
+      with Timing("ran model in ", on_exit=(lambda et: f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU") if DEBUG else None, enabled=timing):
+        logits = self.model(Tensor([toks[start_pos:]]), start_pos).realize()[:, -1, :]
+      with Timing("sync in ", enabled=timing):
+        tok = sample(logits, temperature)
+      start_pos = len(toks)
+      toks.append(tok)
+      output = self.tokenizer.decode(toks)
+    return output
+
+# **** main code ****
+
+if __name__ == "__main__":
+  Tensor.no_grad = True
+  print(f"using {Device.DEFAULT} backend")
+
+  parser = argparse.ArgumentParser(description='Run GPT2 in tinygrad', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--prompt', type=str, default="What is the answer to life, the universe, and everything?", help="Phrase to start with")
+  parser.add_argument('--count', type=int, default=100, help="Max number of tokens to generate")
+  parser.add_argument('--temperature', type=float, default=0.8, help="Temperature in the softmax")
+  parser.add_argument('--model_size', type=str, default="gpt2-medium", help="Size of model to use [gpt2, gpt2-medium, gpt2-large, gpt2-xl]")
+  parser.add_argument('--timing', action='store_true', help="Print timing per token")
+  args = parser.parse_args()
+
+  print(f"using {args.model_size}")
+  gpt2 = GPT2.build(args.model_size)
+  print('Generating text...')
+  y = gpt2.greedy_until(args.prompt, args.count, args.temperature, timing=args.timing)
+  print(y)