diff --git a/README.md b/README.md
index 006e110..489910e 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,20 @@ make train_gpt2cu
 ./train_gpt2cu
 ```
 
-The above lines (1) download the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset, tokenize it with the GPT-2 Tokenizer, (2) save the GPT-2 (124M) weights, (3) init from them in C/CUDA and train for one epoch on tineshakespeare with AdamW (using batch size 4, contenxt length 1024, total of 74 steps), evaluate validation loss, and sample some text.
+The above lines (1) download the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset, tokenize it with the GPT-2 Tokenizer, (2) download and save the GPT-2 (124M) weights, (3) init from them in C/CUDA and train for one epoch on tineshakespeare with AdamW (using batch size 4, context length 1024, total of 74 steps), evaluate validation loss, and sample some text.
+
+## quick start (CPU)
+
+The "I am so GPU poor that I don't even have one" section. No worries, run:
+
+```bash
+python prepro_tinyshakespeare.py
+python train_gpt2.py
+make train_gpt2
+OMP_NUM_THREADS=8 ./train_gpt2
+```
+
+The above lines (1) download the [tinyshakespeare](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) dataset, tokenize it with the GPT-2 Tokenizer, (2) download and save the GPT-2 (124M) weights, (3) init from them in C and train for 40 steps on tineshakespeare with AdamW (using batch size 4, context length only 64), evaluate validation loss, and sample some text. Honestly, unless you have a beefy CPU (and can crank up the number of OMP threads in the launch command), you're not going to get that far on CPU training LLMs, but it might be a good demo/reference.
 
 ## train