Merge pull request #218 from tokk-nv/hotfix/embedded-gsheets

Replace benchmark graphs with SVGs

docs/benchmarks.md

@@ -7,42 +7,42 @@ hide:
 
 ## Large Language Models (LLM)
 
-<iframe width="600" height="371" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=2126319913&format=interactive"></iframe>
+![](./svgs/LLM%20Text%20Generation%20Rate.svg)
 
 For running LLM benchmarks, see the [`MLC`](https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/mlc) container documentation.
 
 ## Small Language Models (SLM)
 
-<iframe width="916" height="507" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=1746097360&format=interactive"></iframe>
+![](./svgs/SLM%20Text%20Generation%20Rate.svg)
 
 Small language models are generally defined as having fewer than 7B parameters *(Llama-7B shown for reference)*   
 For more data and info about running these models, see the [`SLM`](tutorial_slm.md) tutorial and [`MLC`](https://github.com/dusty-nv/jetson-containers/tree/master/packages/llm/mlc) container documentation.
 
 ## Vision Language Models (VLM)
 
-<iframe width="719" height="446" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=88720541&amp;format=interactive"></iframe>
+![](./svgs/Multimodal%20Streaming%20Rate.svg)
 
 This measures the end-to-end pipeline performance for continuous streaming like with [Live Llava](tutorial_live-llava.md).  
 For more data and info about running these models, see the [`NanoVLM`](tutorial_nano-vlm.md) tutorial.
 
 ## Vision Transformers (ViT)
 
-<iframe width="600" height="371" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=702230147&format=interactive"></iframe>
+![](./svgs/Vision%20Transformers.svg)
 
 VIT performance data from [[1]](https://github.com/mit-han-lab/efficientvit#imagenet) [[2]](https://github.com/NVIDIA-AI-IOT/nanoowl#performance)  [[3]](https://github.com/NVIDIA-AI-IOT/nanosam#performance)
 
 ## Stable Diffusion
 
-<iframe width="600" height="371" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=2015943178&format=interactive"></iframe>
+![](./svgs/Stable%20Diffusion.svg)
 
 ## Riva
 
-<iframe width="600" height="371" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=1167153335&format=interactive"></iframe>
+![](./svgs/Riva%20Streaming%20ASR_TTS.svg)
 
 For running Riva benchmarks, see [ASR Performance](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-performance.html) and [TTS Performance](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tts/tts-performance.html).
 
 ## Vector Database
 
-<iframe width="600" height="371" seamless frameborder="0" scrolling="no" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vTJ9lFqOIZSfrdnS_0sa2WahzLbpbAbBCTlS049jpOchMCum1hIk-wE_lcNAmLkrZd0OQrI9IkKBfGp/pubchart?oid=891899240&format=interactive"></iframe>
+![](./svgs/Vector%20Database%20Retrieval.svg)
 
 For running vector database benchmarks, see the [`NanoDB`](https://github.com/dusty-nv/jetson-containers/tree/master/packages/vectordb/nanodb) container documentation.