How Much VRAM to Run Llama 4 Scout?
Llama 4 Scout (17B active / 109B MoE) requires ~34GB VRAM at FP16 for weights alone. With KV-cache for 8K context and a batch of 1, expect 38–42GB. An RTX 4090 24GB cannot fit it at FP16; a pair of A100 40GB can. Q4_K_M quantization brings it to ~12GB — single RTX 4090 feasible.
VRAM requirements by quantization level
| Quantization | VRAM needed | Quality | Fits on |
|---|---|---|---|
| FP16 | 34GB | Full quality | A100 80GB · H100 · 2×RTX 4090 |
| Q8_0 | 18GB | Near-lossless | A100 40GB · RTX 4090 (pair) · L40S |
| Q5_K_M | 12GB | Good | RTX 4090 · RTX 4080 · A5000 |
| Q4_K_M | 10GB | Good | RTX 4090 · RTX 4080 · L4 · RTX 5090 |
| Q3_K_M | 7.5GB | Acceptable | RTX 3090 · RTX 4080 · RTX 4090 |
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Frequently Asked Questions
Can I run Llama 4 Scout on a single RTX 4090?
Yes, at Q4_K_M or Q5_K_M quantization. At FP16 you need at least 34GB VRAM — a pair of RTX 4090s or a single A100 40GB.
Is Q4_K_M quality acceptable for production?
For most tasks (summarization, Q&A, code generation): yes. Quality loss vs FP16 is typically 1-3% on benchmarks. For tasks with very precise reasoning requirements, Q5_K_M or higher is safer.
How does Llama 4 Scout VRAM compare to Llama 3 70B?
Llama 4 Scout uses MoE (Mixture of Experts) — only 17B parameters are active per forward pass even though it has 109B total. This makes it significantly more VRAM-efficient than a dense 70B model, which would need ~140GB at FP16.
Data verified 2026-08-20. GPU rental prices change frequently — verify before purchasing. See methodology.