Is the nVidia RTX 4090 Worth It For Stable Diffusion?

Ai Flux
16 Oct 202220:40

TLDRThe video discusses the Nvidia GeForce RTX 4090, a powerful yet controversial GPU priced at $1600. Highlighting its efficiency, improved ray tracing, and AI capabilities, the host expresses skepticism about Nvidia's claim of a 2x performance boost. The RTX 4090 shows promise for gaming and AI tasks like stable diffusion, but its memory limitations and power connector issues raise concerns. The video suggests waiting for future models for better AI performance, while the current card excels in specific benchmarks but may not justify its high price for all users.

Takeaways

  • 💰 The Nvidia GeForce RTX 4090 is priced at $1600 and is considered a high-end, power-efficient GPU despite its large size and unique shape.
  • 🔥 Initial reviews focused on the GPU's size and gaming benchmarks, highlighting its capabilities in Ray tracing and DLSS3, which are central to its design.
  • 🚀 Nvidia claims a 2x performance increase in power efficiency and AI performance, though this metric might not apply universally and is disputed by the speaker.
  • 🌟 The RTX 4090 features enhanced ray tracing cores, doubling the ray tracing performance, and an improved NVENC co-processor supporting the AV1 codec.
  • 📈 The GPU shows significant performance improvements in games with heavy Ray tracing and triangle counts, like flight simulators and Cyberpunk 2077.
  • 🤖 Some AI features are mentioned as gimmicky, with the Nvidia encoder being the most impressive aspect, not just for gaming but also for AI and streaming applications.
  • 🔌 The RTX 4090 retains a single 12-pin power connector, which has raised concerns about its durability and potential for failure, especially in data center environments.
  • 📊 Benchmarks from Puget Systems indicate a 20-30% improvement in TensorFlow and PyTorch, which are relevant to AI applications like Stable Diffusion.
  • 💡 The RTX 4090 shows impressive double precision performance, which is unusual for RTX GPUs and could be beneficial for certain compute tasks.
  • 🛍️ The high price of the RTX 4090 and its scarcity may be due to market strategies rather than production difficulties, with scalping already affecting its availability.
  • 🔮 For those interested in AI performance, it might be wiser to wait for the next generation of enterprise GPUs, which could offer more significant improvements in memory bandwidth and AI capabilities.

Q & A

  • What is the Nvidia GeForce RTX 4090 and what is its price in the US?

    -The Nvidia GeForce RTX 4090 is a high-performance graphics processing unit (GPU) known for its size and power efficiency. It is priced at 1600 US dollars in the US.

  • What was the initial focus of the RTX 4090 release?

    -The initial focus of the RTX 4090 release was on DLSS3, improving ray tracing, and making incremental improvements in silicon from the previous generation.

  • How does Nvidia claim the performance increase of the RTX 4090 in terms of power efficiency and AI performance?

    -Nvidia claims a bold 2x performance increase in power efficiency and 2x AI performance for the RTX 4090, though the metric may not be universally agreed upon.

  • What improvements have been made to the RTX 4090's ray tracing cores?

    -The RTX 4090 has seen a significant improvement in its ray tracing cores, allowing for 2x the ray tracing performance compared to its predecessors.

  • What is the significance of the RTX 4090's new invinc co-processor supporting AV1?

    -The new invinc co-processor's support for AV1, an open-source codec, is significant as it represents a positive move forward for live video processing.

  • How does the RTX 4090 compare to the previous generation of enterprise cards in terms of video throughput?

    -Despite the RTX 4090's improvements, the previous generation of enterprise cards like the A5000s still have an advantage in video throughput due to driver and in-bank limitations.

  • What are some of the gaming benchmarks that have shown interesting results with the RTX 4090?

    -Gaming benchmarks such as flight simulators and Cyberpunk 2077 have shown interesting results with the RTX 4090, indicating significant performance improvements in games that benefit from ray tracing.

  • What is the controversy surrounding the RTX 4090's power connector?

    -The controversy lies in the RTX 4090's single 12-pin power connector, which is used to push 450 Watts. Concerns have been raised about the durability and reliability of this design, especially in data center environments.

  • What are some of the AI features that Nvidia has introduced with the RTX 4090?

    -Nvidia has introduced some gimmicky AI features with the RTX 4090, which are interesting but may not be significant enough for widespread application. The most impressive feature is the Nvidia encoder, which has seen substantial improvements.

  • How does the RTX 4090 perform in machine learning benchmarks, specifically with TensorFlow and PyTorch?

    -The RTX 4090 shows a significant improvement in machine learning benchmarks, particularly with TensorFlow and PyTorch, which are relevant to applications like stable diffusion. However, the improvements are generally around 20-30%, not the 2x claimed by Nvidia.

  • What is the general consensus on the RTX 4090's price point and its worth for stable diffusion?

    -The general consensus is that the RTX 4090's price point of 1600 US dollars is high, especially considering the incremental improvements it offers. For stable diffusion, it may be more cost-effective to opt for a 3090 or an enterprise-grade card like the A5000, depending on the specific use case and budget.

Outlines

00:00

🚀 Nvidia GeForce RTX 4090 Overview and Initial Impressions

The script introduces the Nvidia GeForce RTX 4090, a powerful and large GPU with a price tag of $1600. It discusses the GPU's efficiency, the initial focus on DLSS3, ray tracing improvements, and AI performance. The reviewer expresses skepticism about Nvidia's claim of a 2x performance increase, highlighting the GPU's new features, such as the enhanced NVENC co-processor supporting AV1 codec. The script also touches on the GPU's limitations compared to the previous generation and the gaming performance improvements due to ray tracing.

05:02

🔌 Power Connector Issues and Market Analysis of RTX 4090

This paragraph delves into the design choice of the RTX 4090's power connector, criticizing the single 12-pin connector used for high power delivery. The narrator shares personal experience with the A5000 series, where power cables failed due to wear and tear. The discussion then shifts to the high price point of the RTX 4090, suggesting that scarcity and scalping are driving factors rather than production difficulties. The paragraph also mentions the lack of released GPU specs and the anticipation of future releases.

10:03

📊 ML Benchmarks and Performance Breakdown for RTX 4090

The script provides an in-depth analysis of machine learning benchmarks for the RTX 4090, referencing sources like Puget Systems for academic insights. It covers supercomputing benchmarks HPL and HPCG, which traditionally do not perform well on consumer GPUs, and highlights the 4090's improved double precision performance. The focus then moves to TensorFlow and PyTorch, which are significant for AI applications like stable diffusion. The benchmarks show a consistent improvement of 20-30% over previous models, with some exceptions.

15:04

🤖 Community Reactions and Advice on GPU Selection

The paragraph discusses community reactions from Reddit and other platforms regarding the RTX 4090's performance, particularly its FP64 capabilities and memory bandwidth limitations. It suggests that while the 4090 is a significant upgrade, it may not be a 2x improvement as claimed by Nvidia. The script advises waiting for potential future releases like the A5000 or A6000 series, which may offer better value or performance for AI workloads, and mentions the current market prices for the 3090 and 4090, noting the scalping issue.

20:06

🔧 Potential Issues and Future Considerations for RTX 4090

The final paragraph addresses potential issues with integrating the RTX 4090 into existing systems, such as compatibility with CUDA 12 and the challenges of fitting the large GPU into data centers or racks. The narrator admits to purchasing one but plans to test and sell it, anticipating a more suitable model in the future. The script concludes by inviting feedback and expressing gratitude for the channel's growth.

Mindmap

Keywords

💡nVidia RTX 4090

The nVidia RTX 4090 is a high-end graphics processing unit (GPU) designed by Nvidia, known for its massive size and power efficiency. In the video, it is discussed as a potential candidate for running AI applications such as Stable Diffusion. The script mentions its price, size, and performance improvements over previous models, with a focus on gaming benchmarks and AI capabilities.

💡DLSS3

DLSS3 stands for Deep Learning Super Sampling 3, a technology developed by Nvidia that enhances the quality and performance of rendered images in games. The script refers to DLSS3 as a focus of the RTX 4090's release, indicating an improvement in ray tracing and overall image quality.

💡Ray Tracing

Ray tracing is a rendering technique used in computer graphics to simulate the physical behavior of light, creating more realistic visuals. The video script highlights the RTX 4090's ray tracing cores and its potential for doubling ray tracing performance, which is significant for both gaming and AI applications.

💡AI Performance

AI performance refers to the capability of a GPU to process and execute machine learning tasks efficiently. The script discusses Nvidia's claim of a 2x AI performance increase with the RTX 4090, which is a key point of contention in the video as the host evaluates whether this claim holds up in real-world scenarios.

💡Encoder

In the context of the video, an encoder is a hardware component that compresses video data for efficient storage or streaming. The Nvidia encoder is highlighted as an impressive feature of the RTX 4090, especially with its support for the AV1 codec, which is significant for video processing and machine learning tasks.

💡CUDA Cores

CUDA cores are the processing units within Nvidia GPUs that are used to accelerate computing tasks, particularly those that can be parallelized. The script mentions incremental improvements in the number of CUDA cores in the RTX 4090, which contributes to its overall performance.

💡Memory Bandwidth

Memory bandwidth refers to the maximum amount of data that can be transferred between the GPU and its memory per second. The video discusses the memory bandwidth of the RTX 4090, noting that it is similar to the 3090 and 3090 Ti, which may limit certain performance improvements.

💡GDDR6X

GDDR6X is a type of high-speed memory used in GPUs to store and quickly access data needed for rendering images. The script mentions GDDR6X as the memory type used in the RTX 4090, which is important for understanding its performance capabilities.

💡Stable Diffusion

Stable Diffusion is an AI model used for generating images from text descriptions. The video is centered around whether the RTX 4090 is worth it for running Stable Diffusion, with benchmarks and performance comparisons provided to assess its suitability.

💡Power Efficiency

Power efficiency is the measure of how well a device uses power to perform tasks, with higher efficiency meaning more performance per watt of power consumed. The script discusses the RTX 4090's power efficiency, noting that despite its size, it is surprisingly efficient.

💡Scalping

Scalping refers to the practice of buying products, often in limited quantities, and reselling them at higher prices. The video mentions that the RTX 4090 is being scalped online, indicating that it is in high demand and difficult to obtain at its original price.

Highlights

The Nvidia GeForce RTX 4090 is a massive and power-efficient GPU with a focus on DLSS3, Ray tracing, and AI performance improvements.

The RTX 4090 boasts a bold claim of 2x performance increase in power efficiency and AI performance, although this metric might not apply universally.

The RTX 4090 features improved Ray tracing cores, delivering 2x the ray tracing performance compared to previous generations.

The new invinc co-processor in the RTX 4090 fully supports the open-source AV1 codec, beneficial for live video processing.

Despite the RTX 4090's advancements, it is still limited by drivers and the in-bank capabilities of the GPUs, affecting video throughput.

Games with heavy Ray tracing and triangle count show significant performance improvements with the RTX 4090.

The RTX 4090's Nvidia encoder is a standout feature, offering impressive capabilities.

Raw specifications of the RTX 4090 show incremental improvements in Cuda cores and memory, but not as groundbreaking as Nvidia claims.

The RTX 4090 retains a single 12-pin power connector, which has raised concerns about its durability and potential for failure.

The price point of $1600 for the RTX 4090 is considered high, with concerns about false scarcity and scalping impacting its availability.

ML benchmarks from Puget Systems indicate a significant improvement in performance for the RTX 4090, especially in double precision tasks.

The RTX 4090 shows a 30% improvement in TensorFlow and PyTorch benchmarks, which are relevant to Stable Diffusion performance.

Eposvox's benchmarks suggest a 30-40% improvement in Stable Diffusion performance with the RTX 4090.

Despite the RTX 4090's performance gains, some users recommend waiting for the next generation of Enterprise GPUs for even better results.

The RTX 4090's architecture focuses more on Ray tracing than AI performance, making it a better fit for gaming and visualization tasks.

Issues with integrating the RTX 4090 into Automatic 1.1 suggest that configuration challenges may arise with new platforms.

The RTX 4090's high price and limited availability may make it a less attractive option for those seeking the best value for AI workloads.