Can You Run LLaMA and Llama-2 Ai Model Locally?
One of the most recent and advanced LLMs is LLaMA, developed by Meta AI. LLaMA is a foundational language model that can be fine-tuned for different domains and applications. It comes in different sizes, ranging from 7 billion to 70 billion parameters, and was trained on a large corpus of text.
You can run the LLaMA and Llama-2 Ai model locally on your own desktop or laptop, but you need to choose the right version of model based on your hardware specifications. Different versions of LLaMA and Llama-2 have different parameters and quantization levels. You also need a decent computer with a powerful GPU with plenty of VRAM, or a modern CPU with enough system memory, to run LLaMA locally.
In this article, we will explore the approach u can use in order to run LLaMA models on your computer.
The native LLaMA model, along with its numerous variations, can be executed locally on consumer-grade hardware in two distinct ways.
- One way is to use the GGML format model and llama.cpp, which rely on the CPU and system memory (RAM) of your device.
- The other way is to use GPTQ model files, which leverages the GPU and video memory (VRAM) of your device.
Both methods have their own advantages and disadvantages depending on your hardware capabilities and preferences.
Can you running LLaMA and Llama-2 locally with GPU?
If you want to use LLaMA AI models on your own computer, you can take advantage of your GPU and run LLaMA with GPTQ file models.
GPTQ is a format that compresses the model parameters to 4-bit, which reduces the VRAM requirements significantly. You can use the oobabooga text generation webui or Exllama, which are a simple interfaces that lets you interact with different LLaMA and Llama-2 versions (Alpaca, Vicuna, Wizard) on your browser. It is pretty easy to set up and run. You can install on Windows or Linux.
For example, currently I am running LLaMA-13B (13 billion parameters) on an RTX 3080 with 10 GB VRAM with very good text quality.
This is a great way to experiment with LLMs on your own hardware and have some fun with text generation.
The advantages of the GPU is that it can significantly improve performance compared to the CPU. Personally, I’ve tried running LLaMA (Wizard-Vicuna-13B-GPTQ 4-bit) on my local machine with RTX 3090; it generates around 20 tokens/s.
However, it’s important to keep in mind that the model (or a quantized version of it) needs to fit into your VRAM if you’re running it on a GPU.
This takes us to the next option.
Can you running LLaMA and Llama-2 locally with CPU?
So, if don’t have good GPU or you’re planning to work with larger models like 30B or 65B and you’re not concerned about compute time, it might be easier to use a CPU and invest in a 64GB or 128GB RAM kit for your PC instead of going for a RTX 3090.
Running LLaMA and Llama-2 model on the CPU with GPTQ format model and llama.cpp is a way to use 4-bit quantization to reduce the memory requirements and speed up the inference.
GGML is a weight quantization method that can be applied to any model. Llama.cpp is a port of Facebook’s LLaMa model in C/C++ that supports various quantization formats and hardware architectures.
Running LLaMa model on the CPU with GGML format model and llama.cpp differs from running it on the GPU in terms of performance and memory usage. According to some benchmarks, running the LLaMa model on the GPU can generate text much faster than on the CPU, but it also requires more VRAM to fit the weights.
Using GGML format model and llama.cpp is cheaper and can be a good option for users who have limited GPU resources or want to run very large models that do not fit in their GPU’s VRAM.
Based on my observations, it appears that there isn’t a strict minimum hardware requirement (within reasonable limits) for running these models. However, the performance can vary depending on the specific model and your hardware setup.
Using LLaMA with a laptop
You can run LLaMA on a laptop. Similar to the desktop you can use both your GPU or CPU. To achieve desktop-level performance would require a high-end mobile GPU like the RTX 3080 with 16GB of GDDR6 on a 256-bit bus, Core i7 processor, at least 32GB of RAM, and good amount of SSD storage.
One good option to run LLaMA on laptop is to use Apple M1 or M2 Pro/Max laptop. Running LLaMA AI on a MacBook with M1 or M2 processor will benefits from Apple’s new unified memory architecture (UMA). It allows the CPU and GPU to access a large pool of fast and low-latency memory without having to transfer data back and forth. This reduces the latency and increases the bandwidth of data access and improves the speed and quality of LLaMA AI’s output. The M1/M2 Pro chip supports up to 200 GB/s of unified memory bandwidth and the M1/M2 Max chip supports up to 400 GB/s of unified memory bandwidth.