Models›Llama 4 Scout 109B MoE
Llama 4 Scout 109B MoE
Meta's next-generation mixture-of-experts model — frontier-class capability at MoE efficiency, requiring 64GB+ RAM.
109B MoE
parameters
64GB
minimum RAM
Overview
What makes Llama 4 Scout 109B MoE notable
Llama 4 Scout is Meta's first mixture-of-experts (MoE) model in the Llama family. Despite having 109B total parameters, it only activates a fraction per token — making it significantly more efficient per inference step than a comparable dense model of the same size.
MoE architecture means it achieves frontier-class quality while running faster than you'd expect from the parameter count. On Apple Silicon with 64GB+ unified memory, it delivers GPT-4 Turbo-class reasoning, nuanced conversation, and complex creative tasks.
The trade-off is hardware: Llama 4 Scout requires 64GB of RAM, which means Mac Studio M4 Max (128GB) or Mac Studio M3 Ultra. It's the right choice for power users who've invested in top-tier hardware and want the best local model available.
Best use cases
What it excels at
- ✓Frontier-class reasoning for complex, multi-step analysis
- ✓Extended context tasks requiring deep comprehension
- ✓Sophisticated creative writing and ideation
- ✓High-stakes professional document review
- ✓Research synthesis across long, complex source material
- ✓Complex coding tasks requiring architectural understanding
Compatibility
Hardware requirements
| Mac model | RAM | Performance | Notes |
|---|---|---|---|
| Mac Studio M4 Max | 128GB | Good | Q4/Q5 quantization — minimum spec for this model |
| Mac Studio M3 Ultra | 192GB+ | Optimal | Q8 full precision — run multiple models simultaneously |
Speed
Approximate tokens/second
Use case fit
Quality ratings
Cost comparison
Without local AI, the equivalent capability costs:
Cloud equivalent
GPT-4o Turbo / Claude 3.5 Sonnet
~$200–330/moper month
Local with Maai Machines
Llama 4 Scout 109B MoE
$0per month
~$10/month electricity. One-time setup.
Run Llama 4 Scout 109B MoE on your own hardware.
Book a consultation. We'll configure this model — and the rest of your stack — in one day.