How much VRAM is needed for Llama 3 70B?

At 4-bit quantization (Q4), Llama 3 70B requires ~42-45 GB of VRAM. It can run on dual 24GB GPUs (e.g. 2x RTX 3090/4090) or an Apple Mac Studio with 64GB+ unified memory.

Updated: August 2026 • Verified v1.2

Local AI / LLM GPU VRAM Calculator

Hardware & SystemsHardware

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Total VRAM = (Params_B * Quant_Bytes) + KV_Cache(Context, Layers) + Overhead_GB

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Understanding Your Results

Calculates total required VRAM broken down by model weights, KV cache memory, and CUDA runtime overhead, matching requirements to specific hardware tiers.

Practical Guidance & Next Steps

4-bit quantization (Q4_K_M) is the standard sweet spot offering 95%+ of full precision intelligence at less than one-third the memory footprint.

What is this?

A hardware planning utility for local AI that calculates the exact video memory required to load and infer open-source LLMs on NVIDIA GPUs and Apple Silicon.

How does it work?

1. Select or slide model parameter size (e.g. 8B, 70B). 2. Choose quantization bit-depth (FP16, Q8, Q5, Q4, Q3). 3. Set your desired context window size (2k to 128k tokens). 4. View total required VRAM and compatible GPU recommendations.

Why use it?

Prevents out-of-memory (OOM) crashes and guides smart hardware purchasing decisions for local AI setups.

Technical Scope & Calculation Limitations

Estimates assume standard GGUF / AWQ architectures. Specialized multi-expert (MoE) architectures with sparse activation may run with lower active memory.