Update README.md

README.md

@@ -43,9 +43,9 @@ We democratize Internet-scale data for financial large language models (FinLLMs)
     | FiQA-SA   | 0.751        | 0.790      | 0.800      | 0.850          | 0.847            | 0.752      |**0.860**      |
     | TFNS      | -            | 0.189      | 0.296      | 0.875          | 0.879           | 0.828       |**0.894**        |
     | NWGI      | -            | 0.449      | 0.503      | **0.642**      | 0.632            |0.583            |0.636            |
-    | Devices   | 512 × A100   | 64 × A100  |  2048 × A100     |  A100     | A100    | A100        |A100        |
-    | Time      | 53 days      | 2.5 days   |  21 days     |  2.36 hours      | 6.47 hours    |   4.15 hours        | 2.47 hours        |   
-    | Cost      | $2.67 million      | $ 14,976   |  $4.23 million    |  $262.4      | $212.2     |  $136.12     |  $262.4     | 
+    | Devices   | 512 × A100   | 64 × A100  |  2048 × A100     | 8 × A100     | A100    | A100        |8 × A100        |
+    | Time      | 53 days      | 2.5 days   |  21 days     |  2 hours      | 6.47 hours    |   4.15 hours        | 2 hours        |   
+    | Cost      | $2.67 million      | $ 14,976   |  $4.23 million    |  $65.6     | $25.88     |  $17.01     |  $65.6     | 
 **Cost per GPU hour.** For A100 GPUs, the AWS p4d.24xlarge instance, equipped with 8 A100 GPUs is used as a benchmark to estimate the costs. Note that BloombergGPT also used p4d.24xlarge As of July 11, 2023, the hourly rate for this instance stands at $32.773. Consequently, the estimated cost per GPU hour comes to $32.77 divided by 8, resulting in approximately **$4.10**. With this value as the reference unit price (1 GPU hour). **BloombergGPT estimated cost= 512 x 53 x 24 = 651,264 GPU hours x $4.10 = $2,670,182.40**
 
   * Reproduce the results by running [benchmarks](./fingpt/FinGPT-v3/benchmark/benchmarks.ipynb), and the detailed tutorial is on the way.