Llama Meta Released 2025-04 visionmultimodalMoE

Llama 4

Meta's MoE multimodal Llama 4 generation. Scout has 10M context. Maverick matches GPT-4o on vision.

Best for vision + long context (10M tokens)
Sizes Scout 17B (109B MoE) · Maverick 17B (400B MoE)
Context 10M
License Llama 4 Community License
Min VRAM (default size, Q4) 64 GB
Rec VRAM 96 GB

What is Llama 4?

Llama 4 is Meta's MoE open-weights language model with vision support. Released in 2025-04, it ships in 2 sizes (Scout 17B (109B MoE), Maverick 17B (400B MoE)) and is licensed Llama 4 Community License. The most popular use case is vision + long context (10M tokens).

VRAM and hardware

The smallest Scout 17B (109B MoE) size needs at least 64 GB VRAM; the largest Maverick 17B (400B MoE) needs around 64 GB.

Size Min VRAM (Q4_K_M) Recommended VRAM Tokens/sec on 3090
Scout 17B (109B MoE) 64 GB 96 GB ~13 tok/s
Maverick 17B (400B MoE) 64 GB 96 GB ~13 tok/s

How to run Llama 4 locally

Option 1: Ollama (simplest)

ollama pull llama-4
ollama run llama-4

Option 2: Mullama (production)

mullama pull llama-4
mullama run llama-4

Option 3: llama.cpp (CLI)

# Download a GGUF from Hugging Face (search "llama-4 gguf")
./llama-cli -m llama-4.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("llama-4.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

What you can build with Llama 4

  • vision
  • long-context
  • agentic
  • multimodal

Hardware it fits on

  • Apple Silicon (8GB): Scout 17B (109B MoE) in MLX
  • Apple Silicon (16GB): Maverick 17B (400B MoE) in MLX
  • Apple Silicon (32GB): Maverick 17B (400B MoE) in MLX or GGUF
  • Apple Silicon (64GB+): Maverick 17B (400B MoE) in MLX
  • RTX 3090 (24GB): Maverick 17B (400B MoE) at Q4_K_M
  • RTX 4090 (24GB): same as 3090, ~30% faster
  • RTX 5090 (32GB): Maverick 17B (400B MoE) at Q8_0
  • 2× RTX 6000 Ada (96GB total): Maverick 17B (400B MoE) at Q4_K_M

Related models in the Llama family

  • Llama 3.1 — 8B, 70B, 405B · general-purpose chat, agentic workflows, code
  • Llama 3.2 — 1B, 3B · low-end hardware, mobile, edge deployment
  • Llama 3.2 Vision — 11B, 90B · vision + chat on desktop GPUs
  • Llama 3.3 — 70B · best-in-class 70B for desktops

Other models you might consider

  • Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware
  • Qwen 2.5-Coder (Alibaba) — 0.5B, best open-weight code model in 2024-2025
  • Qwen 3 (Alibaba) — 0.6B, best all-round local LLM in 2025-2026
  • Qwen 3-Coder (Alibaba) — 30B, long-context code agent workflows
  • DeepSeek R1 (DeepSeek) — 1.5B, reasoning, math, step-by-step problem solving
  • DeepSeek V3 (DeepSeek) — 671B (37B active MoE), frontier-class open-weights LLM (needs data-center GPUs)