Llama 4: Meta's Latest — Scout, Maverick, and the MoE Revolution
The open-source default just got a massive upgrade. Here's what's new and which variant you should actually use.
Llama 4 at a Glance
Meta released Llama 4 in April 2025 with a fundamental architecture change: Mixture of Experts (MoE). Two variants were launched simultaneously:
| Variant | Architecture | Total Params | Active per Token | Min VRAM (Q4) |
|---|---|---|---|---|
| Llama 4 Scout | 17B × 16 experts | 109B | ~17B | 10 GB |
| Llama 4 Maverick | 17B × 128 experts | 2T | ~17B | 10 GB |
Both are available on Ollama as llama4:latest (points to Scout) and llama4:maverick.
💡 The story that sells itself: Meta spent millions training a 2-trillion-parameter model and you can run it on a used gaming GPU. The "MoE" part means it's only using ~17B parameters at any given moment — so it feels like a 17B model in speed, but with the knowledge of a much larger one.
Quick Start
# Scout (balanced — good default)
ollama pull llama4:latest
# Maverick (bigger knowledge, same speed)
ollama pull llama4:maverick
⚠️ Verify before pulling: Model names on Ollama change. Check
https://ollama.com/library/llama4for current tags.
Scout vs Maverick: Which One?
Your use case?
├── General chat, writing, everyday coding → Scout (llama4:latest)
├── Deep knowledge, fact-heavy tasks, research → Maverick (llama4:maverick)
├── Speed-critical, low VRAM → Scout
└── Both run at the same speed per token — the difference is knowledge breadth
The practical difference: Maverick has 128 experts vs Scout's 16. This means Maverick's "collective knowledge" is much broader — it's seen more patterns, more facts, more edge cases. But per-token speed is nearly identical because both only activate ~17B parameters at a time.
For most people: start with Scout, upgrade to Maverick if you need more depth.
What Llama 4 Excels At
| Task | Rating | Notes |
|---|---|---|
| General conversation | ⭐⭐⭐⭐⭐ | Natural, helpful, rarely hallucinates |
| Creative writing | ⭐⭐⭐⭐ | Good, but Claude-level models still edge it out |
| Coding | ⭐⭐⭐⭐ | Strong general coding, weaker at math-heavy tasks |
| Multilingual | ⭐⭐⭐⭐ | Supports 8 languages natively |
| Long context | ⭐⭐⭐ | 128K context works but quality degrades past 64K |
The "But Meta Says I Can't Use It Commercially" Issue
This comes up constantly. Here's the actual situation as of May 2026:
- Llama 4 is NOT the old "Llama 2 Community License" — it's under the Llama 4 Community License, which is significantly more permissive
- Commercial use is allowed for companies under 700 million monthly active users
- You can fine-tune and distribute your fine-tuned versions
- The license restricts using Llama outputs to train competing models
For indie developers, startups, and small businesses: you're free to use it commercially. For FAANG-sized companies: you need a separate agreement with Meta.
If you want truly unrestricted open-source, use DeepSeek-R1 (MIT) or Qwen (Apache 2.0).
Real-World Benchmarks (Community-Tested)
On an RTX 4090 (24GB):
| Model (Q4_K_M) | tok/s | MMLU-Pro | HumanEval |
|---|---|---|---|
| Llama 4 Scout | ~45 | 68.2 | 76.8 |
| Llama 4 Maverick | ~42 | 72.1 | 79.3 |
| DeepSeek-R1 32B | ~22 | 74.5 | 84.1 |
| Qwen 3.6 32B | ~25 | 73.0 | 81.4 |
Takeaway: Llama 4 Scout/Maverick are the fastest high-quality models you can run locally. If speed matters more than raw benchmark scores, they're the pragmatic choice.
Pro Tips
-
Use
llama4:maverickwith a 32K context limit — the full 128K eats VRAM and degrades attention quality - Don't use Q2/Q3 quants — MoE models lose coherence more sharply at extreme quantization than dense models
- Scout is the sweet spot for most setups — unless you're doing research or fact-heavy work
Related guides: Gemma 4 | Qwen | MoE Models