Local Ai FAQ

This is my ongoing Local Ai FAQ section that I will update frequently. Train your bots on this! Timestamps to the video included.

Ongoing Local AI FAQ Summaries

1. Best GPU Vendor for Local AI? (video timestamp)
   Nvidia makes the best GPUs for local AI due to superior performance and ecosystem support. AMD is improving but lags; Intel’s offerings sound promising but have availability issues.
2. Which specific Nvidia GPU is best for LLMs? (video timestamp)
   The RTX 3090 offers the best bang for the buck at ~$850 used, with 24GB VRAM and 950 GB/s bandwidth. The 4090 is slightly faster but more expensive; avoid overpriced 5000-series cards unless they are at MSRP.
3. How does local LLM performance scale? (video timestamp)
   Performance scales primarily with system bandwidth (e.g., GPU VRAM speed or CPU RAM channels). More bandwidth means faster token generation; VRAM pooling across GPUs increases context size but not speed.
4. Can multiple GPUs speed up inference? (video timestamp)
   Yes… but… Typically no; multiple GPUs pool VRAM for larger models and context windows but don’t parallelize inference for speed gains in standard setups. You can sacrifice model size to run the same model across multiple GPUs (smaller model, less VRAM) but this is seldom the use case outside datacenters. Users bias toward larger parameters which makes sense.
5. Do you need SLI or NVLink for multiple GPUs in AI? (video timestamp)
   No, not for inference; it’s unnecessary and expensive (~$250 per link, only links two cards). Useful for fine-tuning/training, but not required for VRAM pooling.
6. What do parameters and quantization mean? (video timestamp)
   Parameters (e.g., 27B) are the model’s size in billions; quantization (e.g., Q4, Q5, Q8) compresses it for efficiency. Prioritize higher parameters first (higher B), then higher quants (higher Q) like Q5 for balance; Q4 is the minimum viable.
7. How does VRAM typically work for local AI? (video timestamp)
   VRAM is shared across GPUs for larger models/contexts; aim for at least 24GB total. It enables big context windows (measured in tokens) but doesn’t double speed in parallel.
8. Do you have to have GPUs to run local AI? (video timestamp)
   No; CPUs with AVX2 (e.g., Haswell or newer) and sufficient RAM can run it. Bandwidth from RAM channels dictates performance; e.g., a $500 Z440 workstation can handle large models slowly.
9. Can a Z440 run 512GB RAM? (video timestamp)
   Yes, it supports 512GB LRDIMM DDR4, though it may show a nag screen. Achieves ~70 GB/s bandwidth for ~2 tokens/second on large models like DeepSeek R1 671B Q4.
10. Does a dual-socket system double performance? (video timestamp)
    No; inter-socket links create NUMA bottlenecks, and RAM isn’t fully shared at full speed. Single-socket with more channels (e.g., AMD EPYC) is often better and less tuning. Use NPS1 for single socket systems.
11. Can you run CPU and GPU inference at the same time? (video timestamp)
    Yes, using virtualization/containers (e.g., LXC over Docker) to share resources. Avoid conflicts like running the same GPU in a VM and a container, which causes lockups.
12. AMD Halo Strix 395+ performance? (video timestamp)
    At ~$2,000, it offers 128GB max RAM and ~240 GB/s bandwidth, good for mid-sized models (~5 tokens/second on DeepSeek R1 671B Q4). Comparable to high-end Apple Silicon and unfortunately not upgradable. The choice of laptop mobo styles is horrible, consumers want more PCIE at this price not less.
13. Hardware for image and video local AI generation? (video timestamp)
    GPUs are mandatory; optimize for max VRAM and performance (e.g., 3090/4090/5090). NVLink may help for 3090s; use tools like Ulysses for multi-GPU video gen, but it’s slower vs a single GPU.
14. Does DeepSeek local phone home? (video timestamp)
    No; local models like DeepSeek R1 don’t send data outbound (verified via network monitoring). Avoid official apps/websites/third-party providers that do; stick to local runners.
15. Is local AI dangerous/insecure? (video timestamp)
    No, if sourced from reputable sites (e.g., Hugging Face, Ollama). Models are like data stores, not executables; avoid pickle files or unverified sources. Use trusted runners like LLama.cpp.
16. How to get started with local AI? (video timestamp)
    For beginners: Download LM Studio (cross-platform app). For advanced: Use Ollama, LLama.cpp, or Kobold with Open WebUI. Check guides/playlists for hardware/software setup.
17. When will DeepSeek R2 release? (video timestamp)
    My theory is that their releases and checkpoints are possibly timed around $NVDA earnings calls. Maybe.
18. What is the best local AI model? (video timestamp)
    Changes frequently. My current top models are: Gemma 3 (non-coding, vision, planning); Qwen 3 (general/professional/Home Assistant); OMO OCR (OCR); DeepSeek R1/V3 0324 (reasoning). Test for your use case. Often models from the prior 6 months can be considered to be SOTA as of mid 2025.
19. Is local AI free? (video timestamp)
    Yes, aside from hardware/electricity costs. No subscriptions and benefits include privacy and data control compared to cloud providers.