Llama 4 Review — The Best Open-Source LLM Yet?

Meta's Llama series has been the backbone of the open-source AI ecosystem since Llama 1 dropped in 2023. Llama 2 became the benchmark for fine-tuning experiments. Llama 3.1 405B proved open-weights models could rival GPT-4. Now Llama 4 — released in April 2026 — makes the boldest leap yet: a Mixture-of-Experts architecture, 10 million token context, and native multimodal support built in from the ground up.

This is a hands-on review of what Llama 4 Scout and Llama 4 Maverick actually do, how they run locally, and whether they deserve the hype.

The Two Models You Need to Know

Meta released two Llama 4 variants for general use:

Llama 4 Scout is the efficient, locally-runnable version. 17 billion active parameters (109B total in MoE form), 10 million token context window, natively multimodal. It runs in approximately 8 GB of VRAM in 4-bit quantization — putting it on RTX 3080/4060-class hardware. This is the model most local AI users care about. Llama 4 Maverick is the powerhouse. 17 billion active parameters (400B total), also multimodal. It needs 24–40 GB VRAM for reasonable inference speeds. You'll want a 4090, two 3090s, or a Mac Studio with 96–192 GB unified memory.

There's also Llama 4 Behemoth, a 288B active parameter giant (2 trillion total) that Meta uses for training and research. It doesn't run locally — it's in the same weight class as frontier proprietary models.

What MoE Actually Means for You

Mixture-of-Experts is why Scout's performance is remarkable for its VRAM footprint. Instead of activating all parameters for every token, Scout activates only 17B parameters per forward pass — different "expert" networks activate depending on the input. The model carries 109B total weights, but inference speed and memory requirements match a 17B dense model.

Practically, this means:

• Speed: Scout generates tokens faster than comparable dense 17B models
• Quality: You get reasoning quality closer to much larger dense models
• VRAM: 8 GB at 4-bit — same as Llama 3.1 8B but dramatically smarter

The trade-off: model file size is large (the full MoE weights), and loading time is longer. On NVMe SSDs, Scout loads in about 25 seconds vs 8 seconds for a 7B model.

Multimodal in Practice

Scout and Maverick can see images natively without any adapter or additional model. You send an image alongside text and the model reasons about both.

Tested use cases that work well:

• Code screenshots — paste a screenshot of an error in your terminal; Scout correctly identifies the issue and suggests a fix
• Diagram understanding — architecture diagrams, flowcharts, whiteboard photos all process cleanly
• Document OCR — feeding scanned documents or PDFs-as-images and extracting structured data
• UI debugging — screenshot a UI component and ask "why doesn't this look right?" gets actionable responses

What to set realistic expectations on: complex mathematical equations in images, hand-drawn sketches, and low-resolution photos under 300×300 pixels still trip the model up. Vision is strong but not perfect.

The 10 Million Token Context Window

Scout's 10M context window is, on paper, revolutionary. You can fit entire codebases, full books, or months of chat history in a single context.

In practice, running 10M context locally has limitations:

• Memory: Even at 4-bit, fitting 1M tokens in context requires ~20 GB of KV cache VRAM on top of model weights. Full 10M context requires specialized hardware (80+ GB)
• Speed: Processing 100,000+ tokens takes 5–15 minutes even on fast GPUs
• Quality degradation: Like all transformer models, Scout's attention quality degrades somewhat for tokens very far back in context

Useful sweet spot locally: 128K–512K context. That's enough to include entire files, long conversations, and detailed system prompts without hitting practical memory limits on consumer hardware.

Benchmark Performance

Scout punches above its VRAM weight class. On coding tasks, it outperforms the much larger Llama 3.3 70B. On general knowledge and reasoning, 70B still wins — MoE isn't magic, and a 17B active parameter count still has knowledge limitations. But for coding, document processing, and multimodal tasks, Scout is the better local choice in 2026.

Running It Locally

Via Ollama (easiest):

ollama pull llama4:scout
ollama run llama4:scout "Describe this image" --image /path/to/image.jpg

Via LM Studio: Available in the model browser. Select "Llama 4 Scout (Q4_K_M)" for the best quality/size balance. Hardware requirements:

• Scout at Q4_K_M (≈66 GB file): ~67 GB VRAM — all 109B parameters must be resident
• Maverick at Q4_K_M (≈242 GB file): ~242 GB VRAM (multi-GPU server or 256 GB+ unified memory)
• Scout inference speed on RTX 4090: ~28 t/s at 4-bit
• Scout on M4 Max 48 GB: ~22 t/s

Should You Upgrade From Llama 3.3 70B?

Yes, switch to Scout if you:

• Want multimodal capabilities (Scout is native; 70B needs a separate vision model)
• Care about coding — Scout's HumanEval score is significantly better
• Are on 8–16 GB VRAM where 70B doesn't fit at all
• Want fast inference — Scout runs faster on same hardware

Stick with Llama 3.3 70B if you:

• Do heavy general knowledge work where 70B still scores higher on MMLU
• Already have it running smoothly and don't want to re-download 22 GB
• Don't need vision and your use case benchmarks better with 70B

The Bottom Line

Llama 4 Scout is the best general-purpose local LLM for most users in 2026. It runs on hardware people actually own, adds multimodal capabilities that were a significant limitation of the 3.x series, and delivers coding quality that beats models twice its effective size.

Maverick is for serious power users with high-end hardware or Apple Silicon with 64+ GB unified memory — it competes with Claude 3.5 Sonnet and GPT-4o at significantly lower cost per token.

The open-source ecosystem just leveled up.

Check what hardware you need to run Llama 4 with the VRAM Calculator → Browse all supported models in the Model Library →