Is the nVidia RTX 4090 Worth It For Stable Diffusion?
TLDRThe Nvidia RTX 4090, priced at $1600, is a massive GPU with improved power efficiency and double the ray tracing performance of its predecessors. While boasting a 2x performance increase in power efficiency and AI, the card's size and single 12-pin power connector have raised concerns. Despite impressive gaming benchmarks, especially with ray tracing, the RTX 4090's real test lies in AI and machine learning tasks, where it shows a significant but not 2x improvement over the RTX 3090. The card's fp64 performance is a standout, but memory bandwidth remains a bottleneck for AI workloads like stable diffusion. With current market scalping, the RTX 4090 may be a tough sell for those seeking substantial AI performance gains.
Takeaways
- 💰 The Nvidia GeForce RTX 4090 is priced at $1600 and is considered a high-end GPU with massive improvements in power efficiency and AI performance.
- 🔌 Despite its size, the RTX 4090 has a single 12-pin power connector, which has raised concerns about its durability and potential for failure.
- 🚀 The RTX 4090 boasts a significant performance increase, especially in double precision floating point capabilities, which is unusual for RTX series GPUs.
- 🎮 The GPU shows impressive performance in gaming benchmarks, particularly in games that utilize ray tracing and have complex graphical demands.
- 📈 Nvidia claims a 2x performance increase in power efficiency and AI, but this claim is met with skepticism as the improvements seem incremental rather than revolutionary.
- 🤖 The RTX 4090 has seen issues with integration into Automatic 11, likely due to configuration challenges and the newness of the platform.
- 🔬 Scientific and supercomputing benchmarks show a performance bump, but not as dramatic as Nvidia's claims, indicating more modest improvements.
- 📊 The RTX 4090's memory bandwidth and VRAM remain the same as the previous generation, which may limit its performance in memory-intensive tasks.
- 💡 The new GPU has shown impressive improvements in benchmarks related to TensorFlow and PyTorch, which are important for AI applications like Stable Diffusion.
- 🛒 The high price and scarcity of the RTX 4090 have led to scalping, with prices significantly higher than the MSRP in some cases.
- 🔮 For those interested in AI performance, it may be worth waiting for the next generation of enterprise GPUs, which could offer more substantial improvements for AI workloads.
Q & A
What is the Nvidia GeForce RTX 4090 and why is it significant?
-The Nvidia GeForce RTX 4090 is a high-end graphics processing unit (GPU) known for its massive size and power efficiency. It's significant due to its focus on DLSS3, improved ray tracing, and claimed 2x performance increase in power efficiency and AI performance, making it a notable release in the field of gaming and AI applications.
How much does the Nvidia RTX 4090 cost and what are some initial reactions to its size?
-The Nvidia RTX 4090 is priced at 1600 US dollars. Initial reactions to its size were focused on its massive and somewhat goofy shape, with many YouTubers making videos highlighting its size, which became a topic of discussion shortly after its release.
What improvements does the RTX 4090 bring to ray tracing performance?
-The RTX 4090 brings a significant improvement to ray tracing performance, with claims of up to 2x the ray tracing performance compared to previous generations. This is attributed to the enhanced ray tracing cores in the GPU.
What is the significance of the RTX 4090's new encoder supporting AV1?
-The new encoder in the RTX 4090 supports AV1, an open-source codec. This is significant as it represents a move forward in live video processing, offering improved video throughput capabilities which are important for visual processing and machine learning applications.
How does the RTX 4090 compare to the previous generation in terms of video throughput and why is this important?
-The RTX 4090 still has limitations compared to the previous generation A5000s in terms of video throughput, despite being capable of handling more video data. This is important for applications like machine learning and visual processing that rely heavily on pixel pushing and video data handling.
What are some of the gaming benchmarks that have been used to test the RTX 4090's performance?
-Gaming benchmarks such as flight simulators and Cyberpunk 2077 have been used to test the RTX 4090's performance. These games are known for their heavy use of triangles and ray tracing, allowing for a clear demonstration of the GPU's capabilities.
What are some of the AI features that Nvidia has introduced with the RTX 4090 and what is the general opinion on them?
-Nvidia has introduced some gimmicky AI features with the RTX 4090, which are interesting but not necessarily groundbreaking or highly impactful. The general opinion is that while these features are a part of the new release, they are not the most impressive or noteworthy aspects of the GPU.
What are the raw specifications of the RTX 4090 that contribute to its performance?
-The raw specifications of the RTX 4090 include incremental improvements in CUDA cores, Boost clock, GDDR6X memory amounting to 24 GB, and the memory link width which is similar to that of the 3090 and 3090 TI. These specifications contribute to its overall performance, although they are seen as incremental rather than revolutionary.
What issues have been reported with the power connector of the RTX 4090 and how have some users addressed this?
-The RTX 4090 has a single 12-pin power connector, which has raised concerns about its ability to safely push 450 Watts. Some users have reported issues with the power cables, including deterioration and failure of the connectors. Some have addressed this by creating their own custom cables that are more robust and reliable.
What is the general consensus on the price of the RTX 4090 and its availability?
-The general consensus is that the 1600 US dollar price tag for the RTX 4090 is high, especially considering the expectation that the end of mining would lower prices. Additionally, the GPU is reported to be in limited supply, with instances of scalping online, indicating a potential strategy of creating false scarcity.
What are some of the machine learning benchmarks that have been used to evaluate the RTX 4090's performance for AI applications?
-Machine learning benchmarks such as HPL, HPCG, TensorFlow, and PyTorch have been used to evaluate the RTX 4090's performance for AI applications. These benchmarks focus on different aspects of GPU performance, from supercomputing capabilities to memory bandwidth and matrix calculations.
What improvements in double precision performance does the RTX 4090 offer and why is this significant?
-The RTX 4090 offers a significant improvement in double precision performance, with more than a 2x improvement compared to the 3090. This is significant because double precision performance has traditionally been reduced on RTX GPUs, and this enhancement represents a departure from previous models.
What are some of the limitations of the RTX 4090 when it comes to memory and how do they affect its performance in AI tasks?
-One of the limitations of the RTX 4090 is its memory capacity, which remains at 24 GB. This can affect its performance in AI tasks that require large amounts of VRAM and high memory bandwidth. Despite being an improvement from previous models, it may not meet the needs of all AI applications, especially when compared to enterprise-grade GPUs with higher memory bandwidth.
What is the current situation with the integration of the RTX 4090 into Automatic11 and what are some of the potential issues?
-There have been some issues with integrating the RTX 4090 into Automatic11, likely due to configuration challenges and the new platform's compatibility with existing systems. Some of these issues may be related to CUDA 12, which is part of the new platform's requirements.
What advice is given for those interested in purchasing the RTX 4090 for AI workloads and what are some alternatives?
-The advice given is to consider purchasing a 3090 or an A5000 instead of the RTX 4090 for AI workloads, as they offer better value for money and similar performance improvements. It is also suggested to wait for the next generation of enterprise GPUs, which may offer more significant advancements in AI performance.
Outlines
🚀 Nvidia GeForce RTX 4090 Overview and Initial Impressions
The video discusses the Nvidia GeForce RTX 4090, a powerful GPU with a hefty price tag of $1600. Highlighted are its impressive size and power efficiency, despite early concerns about the potential for power cable issues. The focus of the RTX 4090 is on DLSS3 technology, improved ray tracing, and AI performance, with claims of a 2x performance increase in power efficiency and AI capabilities. The video also mentions the significant improvement in the NVENC co-processor, which now supports the AV1 codec, beneficial for live video streaming. However, the host expresses skepticism about the 2x performance claim and notes that the GPU is still limited by drivers and in-band capabilities compared to the previous generation of enterprise cards.
🔌 Power Connector Concerns and Market Analysis
The script addresses concerns regarding the RTX 4090's single 12-pin power connector, which is used to deliver 450 watts. The host shares personal experience with A5000 GPUs, where the power connectors failed due to deterioration, especially under continuous operation. The video also touches on the high price point of the RTX 4090, suggesting that it may be artificially inflated due to supply scarcity tactics. Additionally, the script mentions the issue of scalping, where the GPU's price is driven up shortly after release.
📊 ML Benchmarks and Performance Insights
The video delves into machine learning benchmarks, referencing Puget Systems as a reliable source for performance data. It discusses various benchmarks such as HPL, HPCG, and others that are more suited for compute-focused GPUs, showing the RTX 4090's improved double precision performance. The script emphasizes the GPU's performance in TensorFlow and PyTorch, which are significant for AI applications like stable diffusion. The benchmarks indicate a performance improvement of 20-30% in these areas, suggesting that while the RTX 4090 is a powerful GPU, it may not offer the 2x performance jump that Nvidia claims.
🤖 Stable Diffusion Performance and Community Perspectives
The script discusses the performance of the RTX 4090 in running stable diffusion models, a key area of interest for many AI enthusiasts. It mentions that while the GPU shows significant improvement, the increase is not as dramatic as Nvidia's claims. The video also highlights community discussions from sources like Reddit, where users note the impressive FP64 performance and the limitations imposed by memory bandwidth. The host suggests that potential buyers might consider purchasing a 3090 or an A5000 instead, as they may offer better value for money.
🛠 Integration Issues and Future GPU Expectations
The final paragraph addresses some of the initial issues users have faced when integrating the RTX 4090 with software like Automatic 11. The host speculates that these issues are likely due to configuration challenges inherent in a new platform. The video concludes with the host's personal stance on the RTX 4090, expressing an intention to test the GPU and then potentially sell it, with an eye on future enterprise-grade GPUs that may offer more substantial improvements in performance and memory bandwidth.
Mindmap
Keywords
💡nVidia RTX 4090
💡DLSS3
💡Ray Tracing
💡AI Performance
💡Encoder
💡CUDA Cores
💡GDDR6X
💡Memory Bandwidth
💡Stable Diffusion
💡FP64 Performance
💡Scalping
Highlights
The Nvidia GeForce RTX 4090 is a massive and power-efficient GPU with a focus on DLSS3, ray tracing, and AI performance.
The RTX 4090 boasts a 2x performance increase in power efficiency and AI capabilities, although this metric might not apply universally.
The new GPU features improved ray tracing cores, delivering up to 2x the ray tracing performance compared to previous generations.
The RTX 4090 includes a significant enhancement to the NVENC co-processor, now fully supporting AV1, an open-source codec beneficial for live video.
Despite its capabilities, the RTX 4090 is still limited by drivers and in-bank capabilities of previous GPUs for video throughput.
Games with heavy ray tracing and triangle counts see substantial performance improvements with the RTX 4090.
The RTX 4090's Nvidia encoder is considered the most impressive aspect, with various AI features being less impactful.
Raw specifications show incremental improvements in CUDA cores and Boost clock, but the memory and link width remain the same as the 3090 and 3090 Ti.
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 affecting availability.
ML benchmarks from Puget Systems show the RTX 4090 performing well in TensorFlow and PyTorch, which are significant for applications like Stable Diffusion.
The RTX 4090 demonstrates improved double precision performance, a departure from previous RTX GPUs.
Memory bandwidth and VRAM size are identified as potential bottlenecks for the RTX 4090, especially for AI workloads.
The RTX 4090's performance in Stable Diffusion benchmarks shows a 30-40% improvement, making it a strong option for those seeking top performance.
The integration of the RTX 4090 into Auto11 has faced some issues, likely due to configuration and the new platform's transition.
Despite the RTX 4090's strengths, some recommend waiting for the next generation of enterprise GPUs for potentially greater improvements.
The RTX 4090 is a significant jump over previous top-end GPUs, particularly for rasterization workloads, but may not offer as much for core AI workloads.
For those looking for the fastest single GPU option, the RTX 4090 is a contender, but waiting for future releases may offer better value.