RTX 3060 12GB vs 4090 🤔 Do You Really Need an RTX 4090 for AI?
TLDRIn this video transcript, the author compares the performance of an RTX 4090 and an RTX 3060 in various AI applications, including text-to-speech, voice conversion, and image generation. Despite the 4090's superior speed, the 3060 offers impressive performance for a budget GPU, especially in text generation tasks. The author also discusses the importance of VRAM for running larger AI models and concludes that the 3060 is a cost-effective choice for AI enthusiasts, planning to build a budget PC for under $500 and test its performance with AI tools in an upcoming video.
Takeaways
- 🔧 The comparison is between the RTX 4090 and RTX 3060 12GB GPUs, focusing on their performance in AI applications.
- 💻 The test platform includes an Intel 13 900k CPU, 64GB of RAM, and the GPUs are swapped in the same slot for consistency.
- 📈 The maximum batch size is utilized for each GPU to prevent bottlenecking the 4090 due to its larger VRAM.
- 🗣️ Taurus TTS text-to-speech software was tested, with the 3060 requiring more gradient accumulation due to its half VRAM capacity.
- 🏓 Training times for Taurus TTS showed a significant difference, with the 4090 being much faster than the 3060, especially on larger datasets.
- 🎤 RVC voice conversion software was tested, revealing surprisingly less difference in training times between the GPUs than expected.
- 🖌️ Stable Diffusion image generation was compared, with the 4090 generating images significantly faster than the 3060, especially for higher resolution outputs.
- 🧠 Local large language models (LLMs) were tested with the GPT-7B model, where the 4090 outperformed the 3060 in tokens per second.
- 💰 Price-to-performance analysis indicated that the 3060 offers more value for money in Taurus TTS, while the 4090 was faster but more expensive.
- 🎮 For gaming purposes, the 3060 might not be suitable for real-time tasks like voice changing due to high GPU usage.
- 🔥 The 3060 performed well overall, offering good performance for its price, and is recommended for budget-conscious users of AI tools.
Q & A
What is the main purpose of the comparison between the 4090 and 30-60 12GB GPUs in the video?
-The main purpose is to evaluate if the budget GPU (3060) can handle the loads associated with AI tools and to compare its performance with the 4090.
What PC specifications were used for the tests?
-The tests were conducted on a PC with a 13 900k Intel CPU, 64 gigabytes of RAM, and the only variable was the GPU (4090 and 3060) being swapped out.
How was the maximum batch size determined for the GPUs during the tests?
-The maximum batch size was set to not hold the 4090 back due to its larger VRAM, using the allowed batch size for each GPU to simulate a practical approach.
Which AI tools were tested in the video?
-The AI tools tested were Taurus TTS for text generation, RVC for voice to voice conversion, Wokada for voice changing, Stable Diffusion for image generation, and a local LLM (like Chat GPT) for text generation.
What was the performance difference observed between the 4090 and 3060 in Taurus TTS training time for a 60-minute dataset?
-The 3060 took around 200 minutes (3 hours), while the 4090 took approximately 36 minutes.
How does the price for performance comparison favor the 3060 over the 4090 for Taurus TTS with a 10-minute dataset?
-To match the price for performance of the 4090, the 3060 would need to cost around $1,013.57, indicating better value for the 3060 in this scenario.
What was surprising about the RVC training time results when comparing the 4090 and 3060?
-The 4090 was not more than two times faster than the 3060, which was unexpected as it should理论上 be around four to five times faster.
How did the 3060 perform in image generation speed using Stable Diffusion 1.5 with the Mania mix model?
-The 3060 took over 20 seconds, while the 4090 took around 4 seconds, showing a significant speed difference.
What parameter limits the GPU performance in running large language models (LLMs) like GPT-7B?
-VRAM is a critical constraint, as more VRAM allows for running larger models with more parameters.
What was the conclusion about the 3060's performance in AI tools?
-The 3060 performed well, offering more value for money than the 4090, especially for users working with AI tools and having a budget constraint.
What is the plan for future content related to the GPUs tested?
-The plan is to build a PC costing $500 or less and test its performance with the AI tools discussed in the video.
Outlines
📊 GPU Comparison: 4090 vs 3060 in AI Applications
The paragraph discusses a comparison between the RTX 4090 and RTX 3060 GPUs in the context of AI applications. The author tests the performance of both GPUs using various AI tools such as Taurus TTS for text generation, RVC for voice conversion, and Stable Diffusion for image generation. The comparison includes both inference and training times, as well as price-to-performance ratios. The 4090 outperforms the 3060 in most tests, but the 3060 offers better value for money in some cases. The author also notes the importance of VRAM for running larger models and the potential for future content on building a budget PC.
🎤 Voice Conversion and Image Generation Performance
This paragraph focuses on the performance of the GPUs in voice conversion using RVC and image generation using Stable Diffusion. The author details the training times for different data sets and the inference times for voice conversion. The results show that while the 4090 is faster, the 3060 still offers decent performance. In image generation, the 4090 significantly outperforms the 3060, especially when upscaling images. The author also discusses the impact of VRAM on the ability to run larger models and the practical considerations for users looking to work with these AI tools.
🤖 Large Language Models and Final Thoughts on GPU Performance
The final paragraph discusses the performance of the GPUs in running large language models, specifically the GONACO 7B Lama model. The 4090 generates more tokens per second compared to the 3060, and the price-to-performance ratio is also analyzed. The author reflects on the overall performance of the 3060, noting its surprising capabilities and value, especially for users working with AI tools on a budget. The author concludes with plans to build a cost-effective PC and further explore the performance of these AI tools in an upcoming video.
Mindmap
Keywords
💡GPU
💡AI Tools
💡Performance
💡Batch Size
💡VRAM
💡Inference Time
💡Price for Performance
💡Stable Diffusion
💡Local Large Language Model (LLM)
💡Tortoise TTS
💡RVC
Highlights
Comparing the performance of a 4090 with a 3060 12 gigabyte GPU in AI applications.
The main motivator is to see if a budget GPU can handle AI workloads.
Tests conducted on a PC with a 13 900k Intel CPU and 64 gigabytes of RAM.
The 4090 and 3060 were tested with maximum batch size to avoid holding the 4090 back.
Tortoise TTS, RVC, and stable diffusion are the AI tools tested.
In Tortoise TTS, the 3060 required a gradient accumulation of 10 instead of 5 due to half the VRAM of the 4090.
The 4090 outperforms the 3060 significantly in training time for Tortoise TTS, taking around 36 minutes for a 60-minute dataset versus 200 minutes for the 3060.
For RVC voice conversion, the 4090 was not as much faster as expected, taking 109 minutes for a 60-minute dataset compared to 160 minutes for the 3060.
In image generation with stable diffusion, the 4090 is significantly faster than the 3060, especially for higher resolution images.
The 4090 can handle larger models due to its 24 gigabytes of VRAM compared to the 3060's 12 gigabytes.
The 3060 offers more price for performance in Tortoise TTS, with a better ratio at the 10-minute dataset.
For RVC, the price for performance of the 4090 would need to be much lower to match the 3060.
In text generation with local large language models, the 4090 is over twice as fast as the 3060.
The 3060 performed surprisingly well in many AI tools, offering good value for money.
The 4090's superior performance in image generation and text generation justifies its higher cost.
The 3060 is recommended for those looking for a budget GPU that can still handle AI workloads effectively.
The 4090's VRAM advantage allows it to run larger models, which is crucial for certain AI applications.
The 3060's performance in RVC was surprising, suggesting potential optimizations that could improve results.
The 4090's performance in stable diffusion image generation was notably faster, especially for higher resolutions.
The 4090's price for performance is still high, but its speed advantages in certain tasks may justify the cost for some users.
The 3060 is a good option for those who need a GPU for AI tools but are constrained by budget.