Ask HN: Cheapest hardware to run Llama 2 70B

• exllama

  64 2,609 9.0 Python

  A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.

• petals

  98 8,710 8.3 Python

  🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

  

If you have a lot of money (but not H100/A100 money), get 4090s as they're currently the best bang for your buck on the CUDA side (according to George Hotz). If broke, get multiple second hand 3090s. https://timdettmers.com/2023/01/30/which-gpu-for-deep-learni.... If unwilling to spend any money at all and just want to play around with llama70b, look into petals https://github.com/bigscience-workshop/petals

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• ollama

  203 64,536 9.9 Go

  Get up and running with Llama 3, Mistral, Gemma, and other large language models.

  

The only info I can provide is the table I've seen on: https://github.com/jmorganca/ollama where it states one needs "32 GB to run the 13B models." I would assume you may need a GPU for this.

Related, could someone please point me in the right direction on how to run Wizard Vicuna Uncensored or Llama2 13B locally in Linux? I've been searching for a guide and have not found what I need for a beginner like myself. In the Github I referenced the download is only for Mac at the time. I have a Macbook Pro M1 I can use though it's running Debian.

Thank you.

• llama.cpp

  776 57,463 10.0 C++

  LLM inference in C/C++

Was it from here: https://github.com/ggerganov/llama.cpp

Do you have a guide that you followed and could link it to me or was it just from prior knowledge?

• llama2.rs

  3 983 8.9 Rust

  A fast llama2 decoder in pure Rust.

This code runs Llama2 quantized and unquantized in a roughly minimal way: https://github.com/srush/llama2.rs (though extracting the quantized 70B weights takes a lot of RAM). I'm running the 13B quantized model on ~10-11GB of CPU memory.

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