From 981a7df70d496acfa1e2a56374b1bc89371436d0 Mon Sep 17 00:00:00 2001
From: wang-xinyu <wangxinyu_es@163.com>
Date: Fri, 14 Aug 2020 14:04:36 +0800
Subject: [PATCH] add a tutorial check_fp16_int8_support

---
 README.md                            |  1 +
 tutorials/check_fp16_int8_support.md | 14 ++++++++++++++
 2 files changed, 15 insertions(+)
 create mode 100644 tutorials/check_fp16_int8_support.md

diff --git a/README.md b/README.md
index 724d095..9964753 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
 - [A guide for quickly getting started, taking lenet5 as a demo.](./tutorials/getting_started.md)
 - [Migrating from TensorRT 4 to 7](./tutorials/migrating_from_tensorrt_4_to_7.md)
 - [How to implement multi-GPU processing, taking YOLOv4 as example](./tutorials/multi_GPU_processing.md)
+- [Check if Your GPU support FP16/INT8](./tutorials/check_fp16_int8_support.md)
 - [Frequently Asked Questions (FAQ)](./tutorials/faq.md)
 
 ## Test Environment
diff --git a/tutorials/check_fp16_int8_support.md b/tutorials/check_fp16_int8_support.md
new file mode 100644
index 0000000..745ffd1
--- /dev/null
+++ b/tutorials/check_fp16_int8_support.md
@@ -0,0 +1,14 @@
+# Check if Your GPU Supports FP16/INT8
+
+## 1. check your GPU Compute Capability
+
+visit https://developer.nvidia.com/cuda-gpus#compute and check your GPU compute capability.
+
+For example, GTX1080 is 6.1, Tesla T4 is 7.5.
+
+## 2. check the hardware-precision-matrix
+
+visit https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html#hardware-precision-matrix and check the matrix.
+
+For example, compute capability 6.1 supports FP32 and INT8. 7.5 supports FP32, FP16, INT8, FP16 tensor core, etc.
+