Llama Model Size GB: Storage & VRAM Needs

This page explains how much disk space and RAM/VRAM you need for Meta’s Llama models, and gives you a calculator to estimate the file size in gigabytes from the parameter count and quantization level. You enter the number of parameters (in billions) and the bits per weight, and you get the approximate model file size in GB, plus the RAM/VRAM needed to load it.

Formula

The core formula is simple:

Model size (GB) = Parameters (billions) × Bits per weight ÷ 8

• Parameters: The number of weights in the model, in billions (e.g., 7 for Llama 2 7B, 70 for Llama 2 70B).
• Bits per weight: The precision of each weight. Common values: 16 bits (FP16), 8 bits (INT8), 4 bits (INT4).
• ÷ 8: Converts bits to bytes (8 bits = 1 byte).

The result is the raw file size. To load the model into memory, you need extra overhead for the runtime, activations, and optimizer state, so the calculator adds a 20% buffer for the RAM/VRAM estimate.

Worked Example

Using the default values: 8 billion parameters, FP16 (2 bytes per weight).

1. Model size = 8 B × 2 bytes = 16 GB
2. RAM/VRAM = 16 GB × 1.2 = 19.2 GB

So an 8B model in FP16 needs about 16 GB of disk space and roughly 19-20 GB of RAM/VRAM to run. If you quantize to INT4 (0.5 bytes), the file drops to 4 GB and memory to ~4.8 GB.

Background: What Are Llama Models?

Llama (Large Language Model Meta AI) is a family of open-source large language models released by Meta. The first version appeared in February 2023, followed by Llama 2 in July 2023 and Llama 3 in April 2024. They come in various sizes: 7B, 13B, 70B for Llama 2; 8B, 70B for Llama 3; and later 405B. The number (e.g., 8B) refers to the number of parameters—the adjustable weights that the model uses to generate text. More parameters generally mean better performance, but also larger files and higher hardware requirements.

How Quantization Works

Quantization reduces the precision of the weights, trading a small loss in accuracy for a large reduction in size. The most common formats:

• FP16 (16-bit floating point): Full precision, used for training and high-end inference.
• INT8 (8-bit integer): Half the size of FP16, minimal quality loss.
• INT4 (4-bit integer): Quarter the size of FP16, noticeable but often acceptable quality loss.

For example, a 70B model in FP16 is about 140 GB, which is too large for most consumer GPUs. In INT4, it drops to ~35 GB, fitting on a single 48GB GPU like the RTX A6000, or on a 24GB card with CPU offloading.

How to Choose the Right Size

Your choice depends on your hardware and quality needs:

1. Check your GPU VRAM: Consumer GPUs typically have 8-24 GB. Use the calculator to see which quantization fits.
2. Consider RAM if you have no GPU: You can run models on CPU only, but it’s slower. You need at least the RAM/VRAM estimate.
3. Quality vs. size: If you need the best quality for complex tasks, use FP16 or INT8. For casual use, INT4 is often fine.

A common rule of thumb: for 8B models, INT4 (4GB) runs on a 6GB GPU; FP16 (16GB) needs a 24GB GPU. For 70B, INT4 (35GB) needs a 48GB GPU or CPU offloading.

Common Problems and Misconceptions

Many users confuse file size with VRAM usage. The file size is the raw weights; the VRAM also includes activations, KV cache, and overhead. That’s why the calculator adds 20%. Another misconception is that you can run a 70B model on a 16GB GPU just because the INT4 file is 35GB—that’s still too big. Also, be aware that some model files include extra data (tokenizer, config) that adds a few MB, but that’s negligible compared to the weights.

Comparison of Llama Versions

Here’s a quick comparison of common Llama models and their approximate sizes (FP16):

• Llama 2 7B: ~13 GB
• Llama 2 13B: ~26 GB
• Llama 2 70B: ~140 GB
• Llama 3 8B: ~16 GB
• Llama 3 70B: ~140 GB
• Llama 3.1 405B: ~810 GB

These are raw weight sizes; actual download files may include shards and metadata. Always check the specific model card for exact sizes.

Cost and Hardware Considerations

Running large models has real costs. For cloud GPUs, prices vary, but a 24GB GPU (e.g., RTX 3090) might cost $0.50-$1.00 per hour. A 70B model in INT4 would need a 48GB GPU (e.g., A6000) which is more expensive. For local use, you need to buy the hardware upfront. A 24GB GPU costs $1,500-$3,000; a 48GB GPU can be $5,000+. Alternatively, you can use CPU RAM, but inference will be slow (tokens per second instead of hundreds).

Frequently Asked Questions

How much VRAM do I need for Llama 2 7B?

For FP16, you need about 16 GB of VRAM (14 GB for weights + 2 GB overhead). For INT8, ~8 GB; for INT4, ~4 GB. So a 8GB GPU can run INT4 comfortably, but not FP16.

Can I run a 70B model on a 16GB GPU?

No. Even INT4 needs ~35 GB of memory. You could use CPU offloading, but it will be very slow. For practical use, you need a 48GB GPU or multiple GPUs.

What’s the difference between file size and VRAM?

File size is the storage space for the weights. VRAM is the memory needed to load the model and run inference. VRAM is always larger due to overhead, activations, and KV cache.

Is INT4 quality much worse than FP16?

For most tasks, INT4 is surprisingly good, with only a slight degradation in complex reasoning and factual accuracy. For chat and simple Q&A, many users can’t tell the difference.

How do I know which quantization is used in a downloaded model?

Check the filename: it usually contains ‘fp16’, ‘int8’, ‘int4’, or ‘q4_K_M’ (a specific 4-bit method). The model card on Hugging Face also states the quantization.

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in params | Parameters | B | 8 | 1 | 1000
sel quant | Quantization | FP16=2, INT8=1, INT4=0.5
out gb | Model file size | GB | params * quant | 1
out ram | RAM/VRAM to load | GB | params * quant * 1.2 | 1