Llama 4 Scout Hardware Requirements
Meta's 109B MoE with an extreme context window. The community has largely moved to Qwen and GLM — and Meta has pivoted away from open releases — but Scout still earns its keep on unified-memory boxes.
VRAM needed (Q4, 8k context)
68.8 GB
No single consumer GPU fits this model — see multi-GPU and Mac options below.
Updated July 2026. Estimates — see methodology below.
VRAM by Quantization
Weights + KV cache at 8k context + 1.2GB system overhead. Q4_K_M is the community default — quality loss is negligible for most use.
| Quantization | Weights | KV cache (8k) | Total VRAM | Cheapest GPU that fits |
|---|---|---|---|---|
| Q4_K_M Recommended — near-lossless for most use, half the size of Q8 | 66.1 GB | 1.5 GB | 68.8 GB | Multi-GPU / Mac territory |
| Q5_K_M Slightly higher quality than Q4 for ~18% more VRAM | 77.7 GB | 1.5 GB | 80.4 GB | Multi-GPU / Mac territory |
| Q8_0 Effectively lossless — use if you have VRAM to spare | 116 GB | 1.5 GB | 119 GB | Multi-GPU / Mac territory |
| FP16 Full precision — only for fine-tuning or maximum fidelity | 218 GB | 1.5 GB | 221 GB | Multi-GPU / Mac territory |
Longer context costs VRAM
KV cache grows linearly with context: 8k → 1.5 GB · 32k → 6.0 GB · 128k → 24.0 GB. If you plan to feed whole documents or codebases, size your GPU for the context you actually need, not just the weights.
Why Llama 4 Scout is fast but VRAM-hungry
Llama 4 Scout is a Mixture-of-Experts model: all 109B parameters must sit in memory, but each token only activates 17B of them. Memory capacity requirements are those of a 109B model, while speed is that of a 17B model — which is why MoE models feel so fast when they fit, and why Macs with large unified memory punch above their weight running them.
GPU Compatibility (Q4, 8k context)
Every GPU in our database, scored against Llama 4 Scout. Speed is estimated decode rate — memory-bandwidth-bound, so VRAM and bandwidth matter more than shader count.
| GPU | VRAM | Verdict | Est. speed | Price | |
|---|---|---|---|---|---|
| RTX 3060 | 12 GB | Not enough VRAM | — | ~$238 used | |
| Arc B570 | 10 GB | Not enough VRAM | — | from $225 | |
| RTX 4060 | 8 GB | Not enough VRAM | — | ~$275 used | |
| RX 7600 | 8 GB | Not enough VRAM | — | from $250 | |
| Arc B580 | 12 GB | Not enough VRAM | — | from $250 | |
| RX 6700 XT | 12 GB | Not enough VRAM | — | ~$315 used | |
| Arc A770 | 16 GB | Not enough VRAM | — | from $300 | |
| RTX 4060 Ti | 8 GB | Not enough VRAM | — | ~$338 used | |
| RTX 3070 | 8 GB | Not enough VRAM | — | ~$338 used | |
| RTX 5060 | 8 GB | Not enough VRAM | — | from $325 | |
| RX 7700 XT | 12 GB | Not enough VRAM | — | ~$415 used | |
| RX 6800 XT | 16 GB | Not enough VRAM | — | ~$438 used | |
| RTX 3080 | 10 GB | Not enough VRAM | — | ~$463 used | |
| RX 7800 XT | 16 GB | Not enough VRAM | — | ~$488 used | |
| RTX 4070 | 12 GB | Not enough VRAM | — | ~$500 used | |
| RTX 4070 SUPER | 12 GB | Not enough VRAM | — | ~$563 used | |
| RX 7900 XT | 20 GB | Not enough VRAM | — | ~$588 used | |
| RTX 5060 Ti | 16 GB | Not enough VRAM | — | from $550 | |
| RX 9070 | 16 GB | Not enough VRAM | — | from $575 | |
| RTX 5070 | 12 GB | Not enough VRAM | — | from $600 | |
| RX 9070 XT | 16 GB | Not enough VRAM | — | from $600 | |
| RTX 4070 Ti SUPER | 16 GB | Not enough VRAM | — | ~$750 used | |
| RX 7900 XTX | 24 GB | Not enough VRAM | — | ~$838 used | |
| RTX 4080 SUPER | 16 GB | Not enough VRAM | — | ~$900 used | |
| RTX 5070 Ti | 16 GB | Not enough VRAM | — | from $900 | |
| RTX 3090 | 24 GB | Not enough VRAM | — | ~$1,150 used | |
| RTX 5080 | 16 GB | Not enough VRAM | — | from $1,250 | |
| RTX 4090 | 24 GB | Not enough VRAM | — | ~$2,375 used | |
| RTX 5090 | 32 GB | Not enough VRAM | — | from $2,800 |
Run it in one command
With Ollama installed, this pulls the default quant and starts chatting:
Frequently Asked Questions
How much VRAM do I need to run Llama 4 Scout?+
At the recommended Q4_K_M quantization with 8k context, Llama 4 Scout needs roughly 68.8GB of VRAM (66.1GB weights + KV cache + overhead). Q8 needs about 119GB and full FP16 about 221GB.
Can any single consumer GPU run Llama 4 Scout?+
No single consumer GPU currently has enough VRAM to run Llama 4 Scout fully. Realistic options: a multi-GPU rig (e.g. dual 24GB cards), a Mac with enough unified memory, or a 128GB Ryzen AI Max "Strix Halo" mini-PC — the 2026 favorite for exactly this class of model.
Can I run Llama 4 Scout on an RTX 3060?+
No — 12GB is well below what Llama 4 Scout needs even at Q4 quantization.
Can I run Llama 4 Scout on a Mac?+
Yes, if the Mac has enough unified memory: budget roughly 68.8GB of RAM for the Q4 version (plus what macOS itself uses). Apple Silicon runs GGUF models well via Ollama or LM Studio, and MoE models like this one are particularly Mac-friendly — only 17B parameters are active per token, so memory bandwidth goes further.
Can I run Llama 4 Scout on CPU only?+
Sort of. Because only 17B of 109B parameters are active per token, CPU inference is more viable than for dense models this size — expect single-digit tokens/sec with fast DDR5. A GPU is still dramatically better.
Is Llama 4 Scout free for commercial use?+
Yes. Llama 4 Scout is released under the Llama 4 Community License, which permits commercial use.
Related Models
How we calculate these numbers
VRAM = model weights (parameters × bits per weight ÷ 8) + KV cache (architecture-specific bytes per token × context length) + ~1.2GB runtime overhead. Speed estimates assume decode is memory-bandwidth-bound at ~50% utilization (lower for MoE models, which pay routing overhead), matching typical llama.cpp performance on consumer cards; real results vary with runtime, drivers, and settings. Quant sizes reflect GGUF K-quants, which keep some layers at higher precision. Figures are estimates for planning, not guarantees — when in doubt, buy more VRAM than you need today. Prices shown are launch MSRP; mid-2026 street prices often run well above MSRP due to the ongoing memory shortage, and used 24GB cards are holding their value unusually well.