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Local llms with recent training data cutoff

You asked whether there are any local large language models (LLMs) with a very recent training cutoff date or near real-time information retrieval capabilities, and whether these models could run on the hardware shown in the image.

Summary of Your Hardware

  • CPU: Intel Core i7-12700F (12-core, 25MB cache)
  • RAM: 64GB DDR5
  • GPU: AMD Radeon RX 7700 XT (12GB VRAM)
  • Storage: 1.1TB NVMe + 2TB SSD
  • OS: OpenSUSE Tumbleweed (Linux)
  • Filesystem: BTRFS

Local LLMs with Recent Training Cutoffs or Real-Time Information Retrieval

  1. LLaMA 3.1:
  2. Training Cutoff: June 2024 for the latest version.
  3. Hardware Requirements: For smaller models like LLaMA 3.1 (8B or 13B), your system should be able to handle it, especially with your 12GB VRAM GPU and 64GB RAM. Larger models like the 70B or 405B would require multi-GPU setups and more VRAM than what you have.
  4. Real-Time Retrieval: LLaMA models do not have built-in real-time retrieval capabilities but can be fine-tuned or integrated with external tools to access real-time data[5][7].
  5. GPT4All:
  6. Training Cutoff: Various open-source models available, some of which are quite recent (2023).
  7. Hardware Requirements: GPT4All is optimized for consumer hardware, including AMD GPUs like your RX 7700 XT. It can run efficiently on your system without needing internet access[6].
  8. Real-Time Retrieval: GPT4All does not natively support real-time information retrieval but can be customized to work with local documents or external APIs.
  9. Mistral 7B:
  10. Training Cutoff: Released in September 2023, this model is one of the most efficient open-source LLMs.
  11. Hardware Requirements: Your system should handle Mistral 7B comfortably due to its lower parameter count and efficient architecture[3].
  12. Real-Time Retrieval: Like other open-source models, Mistral does not natively support real-time information retrieval but can be extended with external tools.

Key Considerations for Running LLMs Locally

  • Your AMD Radeon RX 7700 XT GPU has sufficient VRAM (12GB) for running smaller models like LLaMA 3.1 (8B), GPT4All, and Mistral 7B. However, larger models such as LLaMA 70B or Falcon 180B would require more VRAM or multiple GPUs[4][5].
  • Your system's RAM (64GB) is more than sufficient for most local LLMs, especially for inference tasks.
  • The OpenSUSE Tumbleweed Linux distribution is well-suited for running these models since most AI frameworks like PyTorch and TensorFlow are better supported on Linux[4].

Conclusion

Your hardware is capable of running several local LLMs, including LLaMA 3.1 (smaller versions), GPT4All, and Mistral 7B. These models have relatively recent training cutoffs but do not natively support real-time information retrieval. However, you can integrate them with external tools for that functionality if needed.