Llama 4 Scout: VRAM Requirements & Local Setup Guide 2026

Meta's efficient MoE model with 109B total parameters and 17B active per token. Supports an extraordinary 10M token context window.

Provider: Meta
Architecture: Mixture-of-Experts (MoE)
Total Parameters: 109B
Active Params/Token: 17B
Context Window: 10M tokens
License: open
Modalities: text, image
HuggingFace: meta-llama/Llama-4-Scout-17B-16E-Instruct

VRAM Requirements by Quantization & Context Length

All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.

Quantization	4K ctx	8K ctx	32K ctx	128K ctx
Q4_K_M	64.2 GB	64.9 GB	66.0 GB	70.5 GB
Q5_K_M	74.6 GB	75.3 GB	76.5 GB	81.0 GB
Q6_K	85.6 GB	86.4 GB	87.5 GB	92.0 GB
Q8_0	110.1 GB	110.9 GB	112.0 GB	116.5 GB
FP16	205.3 GB	206.0 GB	207.2 GB	211.7 GB

Weights = 109B params × GGUF bits per weight (Q4_K_M ≈ 4.9) — all experts are loaded; plus runtime overhead; plus fp16 KV cache from config.json: 8 KV heads × 128 dims (36 of 48 layers capped at a 8,192-token window) — ~192 KB per token at short context. Same engine as the Can I Run LLM calculator.

Best GPU for Llama 4 Scout by Budget

Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.

Under $500 (Entry GPU) Not feasible at this budget
$500–$1,000 (Mid-Range GPU) Not feasible at this budget
$1,000–$3,000 (High-End GPU) Not feasible at this budget
$3,000+ (Workstation / Cloud) A100 80GB (Q4_K_M)

Benchmark Scores

Scores reported by Meta or verified third-party evaluations. Higher is better for all benchmarks except where noted.

Benchmark	Score
MMLU-Pro	74.3
GPQA Diamond	57.2
LiveCodeBench	32.8
Arena Elo (Text)	1279
MMMU	73.4
Arena Elo (Vision)	1118
AIME 2024–25 (Epoch AI)	7.8

How to Run Llama 4 Scout Locally

Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.

Option A — Ollama (Recommended for Beginners)

Ollama is the easiest way to run Llama 4 Scout locally. It handles model downloading, GGUF quantization selection, and serving automatically.

Install Ollama from ollama.com
Pull the model:
```
ollama pull llama4:scout
```
Run interactively:
```
ollama run llama4:scout
```

Option B — HuggingFace Hub

For more control over quantization format, download directly from HuggingFace:

pip install huggingface_hub
huggingface-cli download meta-llama/Llama-4-Scout-17B-16E-Instruct

Hardware note: Check the VRAM table above before downloading. Running Llama 4 Scout requires substantial GPU memory — use Q4_K_M quantization for the lowest VRAM footprint while retaining most model quality.

Strengths and Use Cases

Uniquely suited for tasks requiring extremely long context — entire codebases, large document sets. Efficient to run despite large total parameter count.

When choosing a local LLM, Llama 4 Scout is worth considering if your workload aligns with its design goals. As a Mixture-of-Experts (MoE) architecture from Meta, it offers a distinct trade-off between compute efficiency and capability. With MoE architecture, only a fraction of parameters are active at inference time, dramatically reducing VRAM requirements compared to a dense model of equivalent total size. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.

For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.

Ready to Check Your Hardware?

Use our interactive calculator to see exactly whether your GPU can run Llama 4 Scout — and at what quantization level.

Check if your GPU can run this model → Compare on LLM Leaderboard

Related Tools

🏆 LLM Leaderboard 💻 Can I Run LLM? 🎯 Can I Fine-Tune LLM? 📱 What GPU Do I Need?