feat: Added M1 Max Results (#2)

Author: AtakanTekparmak

README.md

@@ -69,6 +69,48 @@ to [raw_results.txt](raw_results.txt).
     </tbody>
 </table>
 
+<table>
+<thead>
+<tr>
+<th colspan="4">M1 Max (10 CPU core, 32 GPU core, 64 GB RAM) </th>
+</tr>
+</thead>
+    <thead>
+        <tr>
+            <th>Benchmark</th>
+            <th>PyTorch time (s)</th>
+            <th>MLX time (s)</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>Training a transformer <br> language model</td>
+            <td> 1106.75 </td>
+            <td> 752.25 </td>
+        </tr>
+        <tr>
+            <td>Training BERT</td>
+            <td> 793.67 </td>
+            <td> 499.34 </td>
+        </tr>
+        <tr>
+            <td>Whisper inference</td>
+            <td> 21.28 </td>
+            <td> 6.95 </td>
+        </tr>
+        <tr>
+            <td>TinyLLama inference</td>
+            <td> 50.98 </td>
+            <td> 20.61 </td>
+        </tr>
+        <tr>
+            <td>CPU/GPU switch</td>
+            <td> 251.71 </td>
+            <td> 214.57 </td>
+        </tr>
+    </tbody>
+</table>
+
 <table>
 <thead>
 <tr>

raw_results.txt

@@ -30,6 +30,7 @@ Framework: pytorch
 Framework: mlx
 	Average: 8.509361457824706s - Median: 8.509169936180115s
 
+
 ---- TinyLLama inference ----
 => Values from ten executions
 
@@ -39,6 +40,7 @@ Framework: pytorch
 Framework: mlx
 	Average: 33.38054447174072s - Median: 33.322925329208374s
 
+
 ---- CPU/GPU switch ----
 => Values from ten executions
 
@@ -50,6 +52,58 @@ Framework: mlx
 
 ==================================================================
 
+============================= M1 Max =============================
+
+---- Training a transformers language model ----
+=> Values from three executions
+
+Framework: pytorch
+	Average: 1106.7549295464829s - Median: 1101.3749282211793s
+
+Framework: mlx
+	Average: 752.2592077255249s - Median: 752.2592812234227s
+
+
+---- Training BERT ----
+=> Values from three executions
+
+Framework: pytorch
+	Average: 793.6759963925679s - Median: 793.610111951828s
+
+Framework: mlx
+	Average: 499.343544960022s - Median: 498.0613958835602s
+
+
+---- Whisper inference ----
+=> Values from ten executions
+
+Framework: pytorch 
+	Average: 21.27653947197935s - Median: 21.204530119895964s
+
+Framework: mlx
+	Average: 6.946336317062378s - Median: 6.937196493148804s
+
+
+---- TinyLLama inference ----
+=> Values from ten executions
+
+Framework: pytorch
+	Average: 50.98743650913239s - Median: 47.73779845237732s
+
+Framework: mlx
+	Average: 20.61291913986206s - Median: 20.613165140151978s
+
+
+---- CPU/GPU switch ----
+=> Values from ten executions
+
+Framework: pytorch
+	Average: 251.71098244190216s - Median: 252.021404504776s
+
+Framework: mlx
+	Average: 214.5735635280609s - Median: 214.57463908195496s
+
+==================================================================
 
 ============================= M3 Max =============================
 
@@ -82,6 +136,7 @@ Framework: pytorch
 Framework: mlx
 	Average: 4.8507798433303835s - Median: 4.839159846305847s
 
+
 ---- TinyLLama inference ----
 => Values from ten executions
 
@@ -91,6 +146,7 @@ Framework: pytorch
 Framework: mlx
 	Average: 15.41469841003418s - Median: 15.389396786689758s
 
+
 ---- CPU/GPU switch ----
 => Values from ten executions
 
@@ -100,4 +156,4 @@ Framework: pytorch
 Framework: mlx
 	Average: 140.5102721452713s - Median: 140.51127195358276s
 
-==================================================================
+==================================================================