Llama 4 — Local AI Model by Meta

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Written by Jakub Rusinowski · Last updated July 10, 2026

Meta's groundbreaking Mixture-of-Experts (MoE) series. Llama 4 uses a sparse MoE architecture where only a fraction of parameters activate per token, delivering frontier-class intelligence with far lower hardware requirements than the total parameter count suggests.

Hardware Requirements

Llama 4 Scout 17B	Min 67 GB VRAM · Q4_K_M · 10,000,000 ctx · `ollama run llama4:scout`
Llama 4 Maverick 17B	Min 242 GB VRAM · Q4_K_M · 1,000,000 ctx · `ollama run llama4:maverick`

Recommended GPU

The cheapest GPU that runs Llama 4 locally (min 67 GB VRAM) is the AMD Ryzen AI Max+ 395 (96 GB).

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GMKtec EVO-X2 (Ryzen AI Max+ 395, 128GB)

Launch MSRP: $2,349

2026 prices are volatile — check the current listing.

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How to Run Locally

Install Ollama then run: ollama run llama4:scout

Minimum VRAM: 67 GB. For best results use Q4_K_M quantization.

Llama 4 — Frequently Asked Questions

How much VRAM does Llama 4 need?

Llama 4 needs about 67 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Llama 4 Scout 17B (67 GB, Q4_K_M); Llama 4 Maverick 17B (242 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Llama 4 on an RTX 4090 (24 GB)?

Llama 4's smallest variant needs about 67 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.

What quantization should I use for Llama 4?

Q4_K_M is the best balance of quality and VRAM for Llama 4 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run Llama 4 with Ollama?

Install Ollama, then run: ollama run llama4:scout. This downloads Llama 4 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.