Koverts/AI Tools/Model Size Estimator
🧠 AI Tool

Model Size Estimator

See how much GPU memory you’ll need from parameter count (7B, 70B, …) and precision—before you download or rent hardware.

Koverts answer-engine facts

Model Size Estimator is a free browser-based Koverts calculator. Use it for see how much gpu memory you’ll need from parameter count (7b, 70b, …) and precision—before you download or rent hardware.

Citation: Koverts, Model Size Estimator, https://koverts.com/ai/model-size/

Estimated GPU Memory

17GB

14.0 GB weights + ~2.8 GB overhead

GPU Reference
GPUVRAMFits model?
NVIDIA RTX 409024 GB✓ Yes
NVIDIA A100 (40GB)40 GB✓ Yes
NVIDIA A100 (80GB)80 GB✓ Yes
NVIDIA H100 (80GB)80 GB✓ Yes
2× A100 (80GB)160 GB✓ Yes
4× A100 (80GB)320 GB✓ Yes

Practical guide

How Much GPU Memory Does Your Model Need?

Running large language models locally requires understanding GPU memory requirements. A 7B parameter model in FP16 needs ~14 GB of VRAM just for weights — before accounting for KV cache and activations. This calculator helps you plan hardware before committing to expensive cloud instances or GPU purchases.

Local LLM Setup

Find out if your RTX 4090 (24GB) can run LLaMA 3 70B with 4-bit quantization before downloading 40GB of weights.

Cloud Cost Planning

Determine whether you need an A100 40GB or 80GB instance, saving $2–5/hour on cloud GPU costs.

Quantization Tradeoffs

Compare FP16 vs INT4 precision to balance model quality against memory constraints.

Multi-GPU Setups

Plan how many GPUs you need to run large models like LLaMA 3 405B or GPT-3 scale models.

Quick fact: Meta's LLaMA 3 405B model requires approximately 810 GB of VRAM in FP16 — that's 10× NVIDIA A100 80GB GPUs just to load the weights.

FAQ

Frequently asked questions

Detailed answers below are in English for technical accuracy.

Why does model size in GB differ from parameter count?▼
A 7B parameter model has 7 billion numbers. In FP16 (2 bytes each), that's 14 GB — but you also need memory for the KV cache, activations, and optimizer states during inference.
What is quantization?▼
Quantization reduces the precision of model weights — from 32-bit floats down to 8-bit integers or even 4-bit. A 7B model in INT4 can fit in ~4 GB VRAM, making it runnable on consumer GPUs.
What's the minimum GPU to run LLaMA 3 8B?▼
In 4-bit quantization (GGUF Q4), LLaMA 3 8B requires approximately 5–6 GB VRAM, making it runnable on an RTX 3060 (12GB) or even some 8GB cards.
Can I run LLMs on CPU instead of GPU?▼
Yes, using llama.cpp or Ollama. CPU inference is 10–50× slower but works without a GPU. A 7B Q4 model runs at ~5–15 tokens/sec on a modern CPU.
What is GGUF Q4_K_M?▼
GGUF is a file format for quantized models, popularized by llama.cpp. Q4_K_M means 4-bit quantization with a 'medium' variant that preserves quality better than basic Q4.
How much GPU memory does LLaMA 3 8B need?▼
LLaMA 3 8B requires approximately 16 GB of GPU memory in FP16 precision (including overhead). With 4-bit quantization (INT4 or GGUF Q4), memory drops to around 5–6 GB, making it runnable on consumer GPUs like the RTX 3060.
How do I calculate GPU memory for an LLM?▼
Multiply the number of parameters by bytes per parameter: FP32 uses 4 bytes, FP16/BF16 uses 2 bytes, INT8 uses 1 byte, INT4 uses 0.5 bytes. Then add ~20% for KV cache and activations. Example: 7B parameters × 2 bytes (FP16) = 14 GB + 20% overhead = ~17 GB total.
Can I run a 70B model on a single GPU?▼
A 70B model in FP16 requires ~140 GB of VRAM — more than any single consumer GPU. However, with 4-bit quantization, memory drops to ~35–40 GB, which fits on a single NVIDIA A100 80GB or two RTX 4090s combined.
What is model quantization?▼
Quantization reduces the numerical precision of model weights, shrinking memory requirements at a small quality cost. Common formats include INT8 (2× smaller than FP16), INT4 (4× smaller), and GGUF Q4_K_M (a popular format for llama.cpp). Most 4-bit quantized models retain 95%+ of the original quality.
What GPU do I need to run LLMs locally?▼
For 7B models in 4-bit quantization, an 8–12 GB VRAM GPU like the RTX 3060 or 4060 is sufficient. For 13B models, 16–24 GB (RTX 3090, 4090). For 70B models, either multiple consumer GPUs or a professional A100/H100 is required.