Which nVidia GPU is BEST for Local Generative AI and LLMs in 2024?
TLDRThe video discusses the advancements in open source AI and the ease of running local LLMs and generative AI tools like Stable Diffusion. It highlights the importance of Nvidia GPUs for compute-intensive tasks and explores the option of renting versus buying. The video also delves into the latest Nvidia RTX 40 Super Series GPUs, their features, and performance, comparing them to older models. It further discusses the potential of the Nvidia Tensor RT platform for AI inference and shares insights on the capabilities of current consumer-grade GPUs for AI development tasks.
Takeaways
- 🚀 Open source AI has significantly advanced, making it easier to run local LLMs and generative AI like Stable Diffusion for images and video, and transcribe podcasts quickly.
- 💰 In terms of cost and compute efficiency, Nvidia GPUs remain the top choice, with Apple and AMD closing the gap.
- 💡 When considering GPU options, the decision to rent or buy depends on individual needs, especially for those wanting to experiment or conduct in-depth work.
- 🎯 Nvidia's messaging can be confusing, with a variety of products aimed at different markets, but their focus on AI is clear.
- 🤔 The release of the Nvidia RTX 40 Super Series in early 2024 introduced new models with improved performance for gaming and AI tasks, starting at $600.
- 🌟 The new GPUs boast increased compute capabilities, with up to 52 Shader teraflops, 121 RT teraflops, and 836 AI tops.
- 🎮 Nvidia's DLSS technology is a significant feature, allowing for AI-generated pixels to increase resolution without intensive ray tracing, improving gaming performance.
- 💻 The GeForce RTX Super GPUs are positioned as the ultimate way to experience AI on PCs, with Tensor cores providing high throughput for inference applications.
- 📈 Nvidia's Tensor RT is an SDK for high-performance deep learning inference, optimizing runtime and improving efficiency for AI applications.
- 🔍 The script discusses the potential of running large AI models on smaller GPUs through advancements in quantization, making it possible to use models like LLaMA 2 and Cosmos 2 on consumer-grade hardware.
- 🛠️ Creative solutions for utilizing enterprise-grade GPUs in custom setups have been explored, with examples of users successfully running multiple high-end GPUs on a single system.
Q & A
What advancements have been made in open source AI recently?
-Open source AI has seen massive advancements, particularly in running local LLMs and generative AI like Stable Diffusion for images and video, and transcribing entire podcasts in minutes.
What are the best tools for running AI models in terms of compute cost and efficiency?
-Nvidia GPUs are considered the best tools for running AI models in terms of compute cost and efficiency, with Apple and AMD getting closer in competition.
Should one rent or buy GPUs for AI development?
-For those who want to experiment and develop in-depth, buying their own GPU makes more sense than renting from services like RunPod or Tensor Dock.
What is the current status of Nvidia's new GPU releases?
-Nvidia released the new RTX 40 Super Series in early January, which includes the 4080 Super and 4070 Super, aimed at gaming and AI-powered PCs.
What are the key features of the Nvidia RTX 4070 Super GPU?
-The Nvidia RTX 4070 Super GPU offers up to 52 Shader teraflops, 121 RT teraflops, and 836 AI tops, and is designed to supercharge the latest games and form the core of AI-powered PCs.
How does Nvidia's DLSS technology work?
-DLSS (Deep Learning Super Sampling) is a technology that infers pixels to increase resolution without the need for more ray tracing, allowing for AI-generated acceleration of full rate racing by up to four times with better image quality.
What is the significance of Nvidia's Tensor RT platform?
-Tensor RT is an SDK for high-performance deep learning inference, including inference optimizations and runtime that deliver low latency and high throughput for inference applications.
How has model quantization improved in recent times?
-Model quantization has improved to the point where large models can be compressed to fit on smaller GPUs without significant loss of accuracy, enabling efficient running of AI models on consumer-grade hardware.
What are the benefits of using Nvidia's Triton technology?
-Nvidia Triton is a bolt-on improvement for CUDA that optimizes performance for certain applications and improves the deployment of AI models.
What are some of the largest models that have been enabled with Tensor RT?
-Some of the largest models enabled with Tensor RT include Codex LLaMA 70B, Cosmos 2 from Microsoft Research, and the multimodal foundational model, Seamless M4T.
What is the current recommendation for someone looking to buy a GPU for AI development?
-The recommendation depends on the user's needs and budget. For inference tasks, the Nvidia RTX 4070 Super is a good option, while for more intensive development work, the Nvidia RTX 3090 or 4090 may be more suitable.
Outlines
🚀 Advancements in Open Source AI and GPU Options
The paragraph discusses the significant progress in open source AI, particularly highlighting the ease of running local generative AI like Stable Diffusion for images and video, and transcribing podcasts quickly. It emphasizes the importance of Nvidia GPUs for compute tasks, while acknowledging the competition from Apple and AMD. The discussion extends to whether one should rent or buy GPUs, and the considerations for choosing between different models and their costs. The paragraph also mentions the anticipation around the release of new Nvidia GPUs and the impact of TSMC's new facility in Japan on the AI compute market.
💡 Nvidia's New GPU Releases and AI Capabilities
This paragraph focuses on Nvidia's recent RTX 40 Super Series GPU releases, their features, and their positioning in the market. It details the performance improvements of the new GPUs, their AI capabilities, and the price points. The paragraph delves into the specifics of the 4080 Super and 4070 Super models, their suitability for gaming and AI-powered PCs, and the technology behind Nvidia's DLSS and AI tensor cores. It also touches on the concept of model quantization and how it allows for running large AI models on smaller GPUs.
🌐 Nvidia Tensor RT and its Impact on AI Development
The paragraph explores the significance of Nvidia's Tensor RT platform, an SDK for high-performance deep learning inference. It explains how Tensor RT improves efficiency and performance for inference applications, and its integration with other Nvidia technologies. The discussion highlights three major AI models that have been optimized with Tensor RT, including Codex LLaMA 70B, Cosmos 2, and Seismic M4T. The paragraph also mentions the ease of running these models on Windows and the potential performance boosts achieved with Tensor RT.
🔧 Creative Solutions for High-Performance AI Computing
This paragraph narrates a Reddit user's innovative approach to assembling a high-performance AI computing setup using Nvidia A100 GPUs not originally intended for consumer use. It describes the challenges, the technical ingenuity involved in connecting multiple GPUs, and the impressive results achieved. The user's setup, running on a bamboo shelf and using Christian Payne's risers and PCIe switching hardware, demonstrates the potential of such configurations for running large AI models efficiently. The paragraph concludes with advice on purchasing similar hardware and the author's personal recommendations for GPUs based on their experience and budget.
Mindmap
Keywords
💡Open Source AI
💡LLMs (Large Language Models)
💡NVIDIA GPUs
💡Compute Cost
💡RTX 40 Series GPUs
💡DLSS (Deep Learning Super Sampling)
💡Quantization
💡TensorRT
💡NVIDIA A100 GPUs
💡Inference
Highlights
Open source AI has seen massive advancements in the past year, making it easier to run local LLMs and generative AI like Stable Diffusion for images and video, and transcribe entire podcasts in minutes.
Nvidia GPUs are considered the best option in terms of compute cost and versatility, with Apple and AMD getting closer in competition.
The decision between renting or buying GPUs leans towards buying for those who want to experiment and develop with various tools and kits.
Nvidia's messaging can be confusing, with many releases and products aimed at different markets, including enterprise CPUs.
The latest Nvidia GPUs offer great value, but older models may still provide better cost-effectiveness for certain tasks.
Nvidia released the new RTX 40 Super Series in early January, focusing on performance improvements and AI capabilities.
The RTX 40 Super Series GPUs deliver up to 52 Shader teraflops, 121 RT teraflops, and 836 AI tops, enhancing gaming and AI-powered PC computing experiences.
DLSS technology allows for AI-generated pixels, increasing resolution without additional ray tracing, and improving performance and image quality.
Nvidia's Tensor RT is a significant platform for high-performance deep learning inference, offering low latency and high throughput for inference applications.
The new GeForce RTX Super GPUs are positioned as the ultimate way to experience AI on PCs, with Tensor Cores providing the necessary specs for AI tasks.
Nvidia has made progress in LLM quantization, allowing for larger models to be efficiently run on smaller GPUs through techniques like EXL 2.
The 4070 Super GPU, with 16 GB of RAM, is a good option for those focusing on inference and using models rather than developing them, due to advancements in quantization.
The RTX 470 is claimed to be faster than a 3090 at a fraction of the power, but at the same price, making the older 390 series still a strong value option.
Nvidia's Tensor RT has been implemented in major models like Codex, LLaMA 70B, and Cosmos 2, showcasing its capabilities in multilanguage language models and other AI tasks.
There's a trend of using Nvidia's A100 GPUs in unconventional ways, such as stacking multiple GPUs in a single server setup, leading to powerful but complex configurations.
The Reddit user Boris shared a complex setup using five A140 GPUs, demonstrating the potential of such configurations for high-performance AI tasks.
The 4070 and 4080 Super GPUs are recommended for those looking for new options, with the 3090 still being a strong choice due to its affordability.