Charbax | AI inference on Arm Graviton3 at Computex 2024: YouTube Transcription, LLaMA chat and Audio Response @charbax | Uploaded June 2024 | Updated October 2024, 1 week ago.
Nobel Chowdary from Arm presented an insightful demonstration at Computex 2024, showcasing an inference running on an Arm CPU. The demo was split into several parts, each illustrating different capabilities of the Arm CPU in handling complex tasks. The first part focused on YouTube audio transcription. By entering a YouTube video URL, the system downloads the video, processes the audio, and generates a transcript. This process is executed on an AWS Cloud, specifically on a Graviton 3 instance, and it achieves a high degree of accuracy using the OpenAI Whisper model.
The transcription process is efficient, taking only about 4.02 seconds to transcribe a 56-second video, which demonstrates the performance capability of the Graviton 3 CPU. Nobel emphasized that the model does not merely download existing subtitles but actually identifies and transcribes the speech, ensuring accuracy and reliability. This capability supports multiple languages and rivals the transcription quality of other major providers like Google.
Moving on to the second part of the demo, Nobel introduced the LLaMA 2 large language model by Meta, which was also running live on the CPU. This part involved interacting with the LLaMA model, asking questions such as predicting the next U.S. president. Although the model's training only includes data up to 2023, it provides relevant responses based on available information. The demo highlighted performance metrics, such as the time to generate the first token, which is crucial for maintaining user engagement and responsiveness.
Nobel detailed the backend setup, explaining that the demo runs entirely on CPU without relying on GPUs, TPUs, or NPUs. The web application front end was designed by Nobel and is proprietary to Arm, though there are plans to potentially make it more widely available through blogs or other means. This setup underscores the feasibility of running LLMs on CPUs, offering a cost-effective alternative to GPUs while maintaining robust performance.
The third part of the demo combined audio transcription with LLM response generation. Nobel demonstrated this by recording a question through a microphone, converting the audio to text, and then feeding it to the LLaMA model to generate a response. This process also involved measuring performance metrics, which showed impressive speed and efficiency. The system's ability to handle real-time audio input and produce meaningful responses highlights the practical applications of these technologies.
Throughout the demo, Nobel emphasized the practicality and cost savings of using Arm CPUs for AI workloads. The Graviton 3, although not the latest in the Graviton series, showcased significant processing power and efficiency. Nobel mentioned that future iterations, like the Graviton 4, promise even greater performance improvements. This points to a trend where more companies might consider migrating to Arm-based solutions for their AI needs due to the lower total cost of ownership and competitive performance.
The demo also touched on the broader implications of Arm's advancements, suggesting that the ease of integrating these AI models on Arm servers could drive wider adoption. Nobel's work on benchmarking and optimizing performance for different workloads, including databases, further showcases the versatility and potential of Arm CPUs in various computing environments.
In conclusion, the demonstration highlighted Arm's cutting-edge capabilities in AI and machine learning, particularly the feasibility of running advanced models on CPUs. The detailed breakdown of each demo part, from YouTube transcription to LLaMA model interaction and audio transcription, illustrated how Arm's technology can handle diverse and complex tasks efficiently. Nobel's insights into future developments and potential applications suggest a promising direction for AI workloads on Arm architecture.
Description by Chatgpt.
This video was filmed at Computex 2024 in Taipei Taiwan, check out all my Computex videos here: youtube.com/playlist?list=PL7xXqJFxvYvi-qyXBzlB6Wz6PNE8AafRP
My Early Access Members get full access to all my videos as I upload them nightly youtube.com/charbax/join before they are published are the rate of 3-5 videos per day in the days following the event.
Nobel Chowdary from Arm presented an insightful demonstration at Computex 2024, showcasing an inference running on an Arm CPU. The demo was split into several parts, each illustrating different capabilities of the Arm CPU in handling complex tasks. The first part focused on YouTube audio transcription. By entering a YouTube video URL, the system downloads the video, processes the audio, and generates a transcript. This process is executed on an AWS Cloud, specifically on a Graviton 3 instance, and it achieves a high degree of accuracy using the OpenAI Whisper model.
The transcription process is efficient, taking only about 4.02 seconds to transcribe a 56-second video, which demonstrates the performance capability of the Graviton 3 CPU. Nobel emphasized that the model does not merely download existing subtitles but actually identifies and transcribes the speech, ensuring accuracy and reliability. This capability supports multiple languages and rivals the transcription quality of other major providers like Google.
Moving on to the second part of the demo, Nobel introduced the LLaMA 2 large language model by Meta, which was also running live on the CPU. This part involved interacting with the LLaMA model, asking questions such as predicting the next U.S. president. Although the model's training only includes data up to 2023, it provides relevant responses based on available information. The demo highlighted performance metrics, such as the time to generate the first token, which is crucial for maintaining user engagement and responsiveness.
Nobel detailed the backend setup, explaining that the demo runs entirely on CPU without relying on GPUs, TPUs, or NPUs. The web application front end was designed by Nobel and is proprietary to Arm, though there are plans to potentially make it more widely available through blogs or other means. This setup underscores the feasibility of running LLMs on CPUs, offering a cost-effective alternative to GPUs while maintaining robust performance.
The third part of the demo combined audio transcription with LLM response generation. Nobel demonstrated this by recording a question through a microphone, converting the audio to text, and then feeding it to the LLaMA model to generate a response. This process also involved measuring performance metrics, which showed impressive speed and efficiency. The system's ability to handle real-time audio input and produce meaningful responses highlights the practical applications of these technologies.
Throughout the demo, Nobel emphasized the practicality and cost savings of using Arm CPUs for AI workloads. The Graviton 3, although not the latest in the Graviton series, showcased significant processing power and efficiency. Nobel mentioned that future iterations, like the Graviton 4, promise even greater performance improvements. This points to a trend where more companies might consider migrating to Arm-based solutions for their AI needs due to the lower total cost of ownership and competitive performance.
The demo also touched on the broader implications of Arm's advancements, suggesting that the ease of integrating these AI models on Arm servers could drive wider adoption. Nobel's work on benchmarking and optimizing performance for different workloads, including databases, further showcases the versatility and potential of Arm CPUs in various computing environments.
In conclusion, the demonstration highlighted Arm's cutting-edge capabilities in AI and machine learning, particularly the feasibility of running advanced models on CPUs. The detailed breakdown of each demo part, from YouTube transcription to LLaMA model interaction and audio transcription, illustrated how Arm's technology can handle diverse and complex tasks efficiently. Nobel's insights into future developments and potential applications suggest a promising direction for AI workloads on Arm architecture.
Description by Chatgpt.
This video was filmed at Computex 2024 in Taipei Taiwan, check out all my Computex videos here: youtube.com/playlist?list=PL7xXqJFxvYvi-qyXBzlB6Wz6PNE8AafRP
My Early Access Members get full access to all my videos as I upload them nightly youtube.com/charbax/join before they are published are the rate of 3-5 videos per day in the days following the event.
