Graphic Cards for AI
Nvidia and AMD graphic cards are becoming a commodity where pricing has been a huge barrier for even consumers to look into running AI locally at home. What are some metrics I should consider in purchasing graphic cards and what graphic cards is good? Do I only look at VRAM? or do the software stack matter as well like ROCm and CUDA? Open models like Qwen, QwQ, GLM, Llama, Phi and more are all becoming a strong candidate to run these models at home. #ai #graphiccard #technology Chapters 00:00 Intro 00:29 AI on CPU 02:23 AI on GPU 03:08 RTX 3060 04:10 RTX 3090 06:28 Rent GPU 07:20 RTX 4090 09:39 RTX 5090 10:13 AMD 11:18 Conclusion
Video Chapters
- 0:00 The essentials for running AI locally at home
- 0:50 Understanding model sizes, RAM, and quantization
- 2:40 Why even an old GPU outperforms a modern CPU
- 4:03 Why the RTX 3090 is the "Holy Grail" for local AI
- 5:42 Balancing high-end budgets and cloud rental options
- 7:45 The most important metric for running large AI models
- 9:22 Multi-GPU setups and the future of the RTX 50-series
- 10:15 Can AMD cards compete with NVIDIA for AI tasks?
- 11:20 Moving beyond consumer hardware to enterprise solutions
Original Output
0:00 The essentials for running AI locally at home 0:50 Understanding model sizes, RAM, and quantization 2:40 Why even an old GPU outperforms a modern CPU 4:03 Why the RTX 3090 is the "Holy Grail" for local AI 5:42 Balancing high-end budgets and cloud rental options 7:45 The most important metric for running large AI models 9:22 Multi-GPU setups and the future of the RTX 50-series 10:15 Can AMD cards compete with NVIDIA for AI tasks? 11:20 Moving beyond consumer hardware to enterprise solutions Timestamps by StampBot 🤖 (453-graphic-cards-for-ai)
Unprocessed Timestamp Content
0:00 Running AI locally at home; the need for graphic cards to power it. 0:30 You don't actually need a graphic card to run AI models. 0:50 Most usable AI models for local use are around 30 billion parameters. 1:13 Llama 3 8B in native precision requires more than 16GB of RAM. 1:46 Quantized models allow running Llama 3 8B on 5-6GB RAM, but slowly. 2:24 Technically running AI on CPU without a GPU is possible, but silly. 2:40 Even 7-10 year old graphic cards outperform CPUs for AI tensor parallelism. 3:09 Spending $300 on an old RTX 3060 opens up your AI model options. 3:22 Mainstream models are too large for an RTX 3060's VRAM. 4:03 The famous RTX 3090, the holy grail, has 24 GB of VRAM. 4:32 The RTX 3090 allows running 30B, and potentially heavily quantized 70B models. 5:25 The RTX 3090 is five years old, consider its lifespan if used 24/7. 5:42 The $1000-$5000 price point for GPUs offers comparable mainstream AI models. 6:30 At this budget, consider renting data center GPUs by the hour. 7:20 The RTX 4090, a $2000 option, has held its value well. 7:45 VRAM is the most important metric for fitting large AI models. 8:23 In practice, the performance difference between 3090 and 4090 is bottlenecked by memory. 9:22 Many users prefer buying multiple 3090s for more total VRAM capacity. 9:40 The upcoming RTX 5090 will offer more VRAM (32GB) at around $3000. 10:15 Don't sleep on AMD cards; they are cheaper than overpriced NVIDIA cards. 10:57 AMD cards run AI models just as well, once software configuration challenges are met. 11:20 Beyond $5000, you're looking at commercial data center grade GPUs or neoclouds. Timestamps by StampBot 🤖 (453-graphic-cards-for-ai)