RTX 3060 12GB vs 4090 🤔 Do You Really Need an RTX 4090 for AI?

Jarods Journey
12 Aug 202312:02

TLDRIn this video, the presenter compares the RTX 3060 12GB with the RTX 4090 to determine if the latter is necessary for AI tasks. Using the same PC with an Intel 13900k CPU and 64GB RAM, they test various AI applications including Taurus TTS for text generation, RVC for voice conversion, stable diffusion for image generation, and a local large language model. The RTX 3060, despite having half the VRAM of the 4090, performs surprisingly well in most tests, with the 4090 showing significant speed advantages in image generation tasks. The presenter concludes that the RTX 3060 offers great value for money, especially for those on a budget, and plans to build a PC costing under $500 to further explore the capabilities of budget GPUs in AI applications.

Takeaways

  • 🔍 **Comparison of RTX 3060 12GB and RTX 4090**: The video compares the two GPUs in various AI applications to determine if the RTX 4090 is necessary for AI tasks.
  • 💻 **System Configuration**: The tests were conducted on a system with an Intel 13900k CPU and 64GB of RAM, ensuring consistent performance evaluation.
  • 📉 **Batch Size Maximization**: The maximum batch size was used for each GPU to prevent bottlenecking the more powerful RTX 4090.
  • 📈 **Performance in AI Tools**: The RTX 3060 showed surprisingly good performance in AI applications, with the RTX 4090 being faster but not by as much as expected in some cases.
  • 📊 **Tortoise TTS Test Results**: The RTX 3060 required more time for text-to-speech tasks, but the price-performance ratio was better for the 3060.
  • 📝 **RVC Training Time**: The RTX 4090 was not significantly faster than the RTX 3060 in voice conversion tasks, contrary to expectations.
  • 🖼️ **Stable Diffusion Image Generation**: The RTX 4090 outperformed the RTX 3060 in image generation tasks, with a substantial difference in speed.
  • 📱 **Voice Changer Performance**: The voice changer worked faster on the RTX 3060 when reduced settings were used, but at the cost of GPU resources.
  • 💰 **Price-Performance Consideration**: The RTX 3060 offered better price-performance in several tests, making it a more cost-effective choice for certain AI applications.
  • 🧠 **Large Language Model (LLM) Constraints**: The RTX 4090 with more VRAM can handle larger models, which is a significant advantage for running complex LLMs.
  • 📉 **VRAM Limitations on 3060**: The RTX 3060's VRAM limits it to smaller models compared to the RTX 4090, which could be a deciding factor for some users.
  • 🛠️ **Future Build**: The creator plans to build a PC for under $500 and test its performance with AI tools, showcasing the potential of budget-friendly hardware.

Q & A

  • What is the main motivation behind comparing the RTX 4090 and RTX 3060 12GB in the video?

    -The main motivation is to determine if the budget GPU (RTX 3060 12GB) can handle the loads required for AI tools and applications.

  • What is the CPU and RAM configuration used for the tests in the video?

    -The tests were conducted using a PC with a 13 900k Intel CPU and 64 gigabytes of RAM.

  • What is the difference in VRAM between the RTX 4090 and RTX 3060 12GB?

    -The RTX 4090 has twice the VRAM of the RTX 3060 12GB, which is why the 3060 had to do a gradient accumulation of 10 instead of 5 for Tortoise TTS.

  • How much longer does it take for the RTX 3060 to train on a 60-minute dataset compared to the RTX 4090?

    -The RTX 3060 takes around 200 minutes or three hours, whereas the RTX 4090 takes closer to around 36 minutes.

  • What is the price-performance ratio for Tortoise TTS if the RTX 4090 is to match the RTX 3060?

    -To match the price-performance of the RTX 3060, the RTX 4090 would need to be closer to around one thousand thirteen dollars and 57 cents.

  • How does the RTX 4090 perform in RVC training compared to the RTX 3060?

    -The RTX 4090 is not more than two times faster than the RTX 3060 in RVC training, which was surprising given the expected performance difference.

  • What is the approximate time difference for image generation between the RTX 4090 and RTX 3060 using Stable Diffusion 1.5 with the Mana mix model?

    -The RTX 4090 generates images in around 4 seconds, whereas the RTX 3060 takes closer to around 20 seconds.

  • What is the price-performance ratio for Stable Diffusion image generation if the RTX 4090 is to match the RTX 3060?

    -To match the price-performance of the RTX 3060, the RTX 4090 would need to be priced at 844.46 dollars.

  • How does the RTX 4090 perform in generating tokens per second with the Guanaco 7B Lama 2 model?

    -The RTX 4090 generates at about 75 tokens per second, which is 2.71 times faster than the RTX 3060.

  • What is the limitation of the RTX 3060 when it comes to running large language models due to its VRAM?

    -The RTX 3060, with its 12GB of VRAM, is only able to run up to 13 billion parameter models, compared to the 24GB VRAM of the RTX 4090 which allows for 33 billion parameter models.

  • What is the conclusion about the RTX 3060 12GB in terms of its performance and value for AI applications?

    -The RTX 3060 12GB performed well in many AI tools and offers more bang for the buck compared to the RTX 4090, making it a good choice for budget-conscious users.

  • What future content is planned by the creator regarding the RTX 3060 and AI tools?

    -The creator plans to build a PC costing $500 or less and test its performance with AI tools in an upcoming video.

Outlines

00:00

🤖 GPU Comparison for AI Tools: 4090 vs 3060

The video script begins with a comparison between two graphics processing units (GPUs), the 4090 and a 30,60 12 gigabyte, to evaluate their performance in AI applications. The comparison is motivated by a desire to assess if a budget GPU can handle the workloads of AI tools. The tests are conducted on the same PC with a 13 900k Intel CPU and 64 gigabytes of RAM, swapping out the 4090 for the 3060 to ensure consistent conditions. The batch size for each GPU is maximized to prevent bottlenecking the 4090's superior VRAM. The tools tested include Taurus TTS for text generation, RVC for voice conversion, Wokada for voice changing, and Stable Diffusion for image generation. Additionally, a local large language model (LLM) is compared in terms of tokens per second. The video also provides a side-by-side comparison of generating a 41-word prompt and discusses the training time for different data sets. The results show that while the 3060 performs well, the 4090 is faster, especially in larger data sets, although the price-performance ratio is more favorable for the 3060.

05:01

🚀 Performance and Price-Performance Analysis

The second paragraph delves into the performance and price-performance analysis of the GPUs when using RVC (Retrieval-based Voice Conversion) software. The narrator expresses surprise at the results, as the 4090 did not perform as much faster as expected, possibly due to unoptimized settings or lack of specific adjustments. The delay test for the voice changer is demonstrated, showing the trade-off between speed and GPU usage. When comparing the two GPUs in terms of image generation with Stable Diffusion, the 4090 outperforms the 3060 significantly, especially in higher resolution image generation tasks. The price-performance ratio is also calculated for image generation tasks, with the 4090 being faster but more expensive. The narrator highlights the importance of VRAM for running larger models and the 4090's capability to handle larger models effectively.

10:01

💡 Final Thoughts and Future Plans

In the final paragraph, the narrator summarizes the performance of the RTX 3060, noting that it performed better than expected across many of the AI tools tested. The 3060 is praised for its value, often available at a much lower price point, making it a more cost-effective choice for users with a budget. The narrator shares plans to build a PC costing $500 or less and test its performance with AI tools in an upcoming video. The video concludes with a mention of affiliate links for GPU upgrades and an expression of gratitude towards the viewers for their support.

Mindmap

Keywords

💡RTX 3060 12GB

The RTX 3060 12GB is a graphics processing unit (GPU) developed by Nvidia, designed for gaming and other graphics-intensive tasks. In the video, it is compared with the RTX 4090 to evaluate its performance in AI-related tasks. The 12GB refers to the video memory (VRAM) that the GPU has, which is crucial for handling large datasets and complex AI models. The RTX 3060 is positioned as a budget GPU, making it an interesting option for those looking for a cost-effective solution for AI workloads.

💡RTX 4090

The RTX 4090 is a high-end GPU from Nvidia, part of the same RTX series as the 3060 but with more advanced features and higher performance. It is used in the video as a benchmark to compare against the RTX 3060. The RTX 4090 is known for its large VRAM and high-speed performance, making it a popular choice for enthusiasts and professionals working with demanding applications like AI and machine learning.

💡AI Tools

AI tools refer to software applications that utilize artificial intelligence to perform tasks such as text generation, voice conversion, and image generation. In the context of the video, AI tools are used to test and compare the capabilities of the RTX 3060 and RTX 4090 GPUs. Examples of AI tools mentioned include Taurus TTS for text-to-speech, RVC for voice conversion, and stable diffusion for image generation.

💡Batch Size

Batch size in the context of AI and machine learning refers to the number of samples processed at one time by the GPU. The video mentions using the maximum batch size allowed for each GPU to ensure a fair comparison. A larger batch size can lead to faster training times but requires more VRAM, which is why the RTX 4090, with its greater VRAM, can handle a larger batch size than the RTX 3060.

💡VRAM

Video RAM (VRAM) is the memory used by the GPU to store image data for rendering or processing graphics. The amount of VRAM can significantly impact the performance of a GPU, especially when handling AI tasks that require processing large datasets. The RTX 3060 has 12GB of VRAM, while the RTX 4090 has more, allowing it to process larger batches and more complex models.

💡Tortoise TTS

Tortoise TTS is a text-to-speech software mentioned in the video for generating audio files from text prompts. It is used as one of the AI tools to test the performance of the RTX 3060 and RTX 4090. The software allows for customization, such as using a voice that a user has trained, making it a versatile tool for various applications.

💡RVC

RVC, or Retrieval-based Voice Conversion, is an AI tool used in the video for converting one voice to another. It is highlighted as a tool that has been used to create AI covers that are popular on the internet. The performance of RVC on both the RTX 3060 and RTX 4090 is compared to demonstrate the difference in processing speeds for voice conversion tasks.

💡Stable Diffusion

Stable Diffusion is an AI image generation model tested in the video using different models like Mana mix for anime images and SDXL for high-resolution images. It is used to compare the image generation capabilities and speeds of the RTX 3060 and RTX 4090. The model uses a diffusion process to create images by subtracting noise, showcasing the GPUs' ability to handle complex image processing tasks.

💡Local LLM

Local Large Language Models (LLMs) refer to large-scale AI models that are run on local machines rather than cloud-based services. In the video, the host uses a local LLM called 'Guanaco 7B Lama 2' for text generation tasks. The speed and efficiency of text generation on both GPUs are compared, highlighting the importance of VRAM in running such large models.

💡Price for Performance

Price for performance is a衡量标准 used to evaluate the value of a product based on its cost versus its performance capabilities. The video discusses the price for performance of the RTX 3060 and RTX 4090 in the context of AI tasks, suggesting that the RTX 3060 offers a better price for performance in certain scenarios due to its lower cost and sufficient performance for the tasks tested.

💡Gradient Accumulation

Gradient accumulation is a technique used in machine learning to train large models with limited VRAM. It involves breaking down the input into smaller batches and accumulating the gradients over multiple forward and backward passes. The video mentions using gradient accumulation of 10 for the RTX 3060 instead of 5 for the RTX 4090 due to the difference in their VRAM capacities.

Highlights

Comparison of RTX 4090 and RTX 3060 12GB GPUs in AI applications.

RTX 3060 12GB demonstrated capability in handling AI workloads, contrary to expectations.

Both GPUs were tested on the same PC with an Intel 13900k CPU and 64GB RAM.

Maximum batch sizes were used for each GPU to prevent bottlenecking the RTX 4090.

Tortoise TTS text generation software showed the RTX 3060 performing close to the RTX 4090 with a gradient accumulation of 10.

RTX 3060 took longer in training times compared to RTX 4090 across different data sets.

Price-performance comparison favored the RTX 3060 for Tortoise TTS inference.

RVC (Retrieval-based Voice Conversion) showed surprising results with the RTX 4090 not being significantly faster than the RTX 3060.

Optimization issues might be affecting RVC performance on the RTX 4090.

Voice changer delay tests showed the RTX 3060 utilizing most of its graphics card resources.

Stable Diffusion image generation tests revealed the RTX 4090 to be faster, especially for higher resolution images.

Price-performance analysis showed the RTX 4090 to be 4.2 times faster but at a higher cost.

Local large language models (LLMs) tests showed a significant speed difference, with RTX 4090 generating 75 tokens per second compared to RTX 3060's 28 tokens per second.

VRAM constraints are important for LLMs, with RTX 4090 being able to run larger models.

The RTX 3060 offers a good balance of price and performance for many AI tools.

The RTX 3060 is often available at a significantly lower price point, providing excellent value.

Upcoming video will feature building a PC under $500 and testing its performance with AI tools.