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graphcore-research · Aug 14, 2024 · 4a84fcf · 4a84fcf
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diff --git a/docs/JAX FP8 matmul tutorial.ipynb b/docs/JAX FP8 matmul tutorial.ipynb
@@ -13,7 +13,16 @@
    "id": "f878aaba-ce22-42d3-89e3-ad7f22b6f75c",
    "metadata": {},
    "source": [
-    "## FP8 in machine learning quickstart\n",
+    "## Quickstart: FP8 in deep learning\n",
+    "\n",
+    "The latest generation of machine learning hardware (Nvidia H100, AMD MI300, Graphcore C600, ...) have integrated direct FP8 support in the hardware, improving energy efficiency and throughput.\n",
+    "\n",
+    "![image](img/fp-formats.webp)\n",
+    "\n",
+    "\n",
+    "\n",
+    "### References\n",
+    "\n",
     "\n",
     "* Two FP8 datatypes;\n",
     "* Why?\n",

diff --git a/docs/img/fp-formats.webp b/docs/img/fp-formats.webp