[2026 Local AI] Meta Llama 4: What Do You Need to Run It on Your PC? (MoE Model Hardware Guide)

That's right! In 2026, you can't leave out Meta's latest flagship: Llama 4 (Scout, Maverick, etc.). Llama 4 has a fundamentally different architecture from previous generations, which means hardware requirements have entered an entirely new phase.

Here's a breakdown of Llama 4's core features, hardware specs, and a blog planning guide for covering it.

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Llama 4: What's Different, and What Hardware Do You Need?

The biggest feature of Llama 4 is the adoption of MoE (Mixture-of-Experts) architecture and native multimodality (simultaneous visual/audio processing).

• The MoE Catch (Fast but Memory-Hungry)

• For example, the Llama 4 'Scout' model has a total of approximately 109B (109 billion) parameters, but during inference, only 17B parameters are actually activated.

• Result: Generation speed is blazingly fast like a 17B model, but the entire model must be loaded into memory, so VRAM (memory) usage is on par with a 109B-class model.

Recommended Hardware Specs for Running Llama 4

A typical single gaming PC (with 8-24GB VRAM) won't cut it for smooth local operation — you'll need a high-end workstation.

• VRAM Required: Approximately 60GB to 70GB (with GGUF 4-bit quantization)

• Recommended PC Setup (Multi-GPU): 3 to 4 NVIDIA RTX 3090 / 4090 (24GB each) linked together

• Recommended Mac Setup (Best Value): Mac Studio (M2/M3/M4 Ultra, Unified Memory 128GB or more)

• Tip: For running large MoE models like Llama 4 locally, Apple Silicon (Mac), which shares RAM with the GPU, offers overwhelmingly superior value for money.

• Cloud/API Recommendation: If the hardware investment is too burdensome, calling the model via API from services like Groq, Together AI, or AWS is the most practical option. (You can experience incredible speeds of hundreds of tokens per second.)