Llama 4 Maverick

Positioning

Llama 4 Maverick is the model you run when you have a Mac Studio M2/M3 Ultra with 192+ GB unified memory, a workstation with 80+ GB VRAM across dual cards, or an H100. Same active-parameter footprint as Scout (~17B per token) but a much larger expert pool — quality lifts noticeably on hard tasks.

Strengths

• Frontier-adjacent quality for an open-weight model — closes most of the remaining gap with closed models on the GPT-4-class workload mix.
• MoE compute story remains favorable — only 17B active per token means 8–15 tok/s on properly-resourced hardware despite the 400B nameplate.
• Native multimodal like Scout, but the larger expert pool gives better dense reasoning on charts, tables, and code-with-screenshot workflows.

Limitations

• 400B total parameters — disk footprint at Q4 is ~225 GB, working set similar. This is "do you own a workstation" hardware.
• MoE quality at very low quants drops faster than dense models — Q3 and below show degraded routing decisions; Q4 minimum.
• License audit recommended before commercial deployment given Llama 4's revised AUP.

Real-world performance on RTX 4090

• Q4_K_M (~225 GB) — not realistically runnable on 4090 even with offload; system RAM bandwidth becomes the bottleneck
• Q3_K_M (~165 GB) — possible on dual 4090 + 192 GB DDR5, ~3–5 tok/s; not recommended (quality cliff)
• Comfortable on: Mac Studio M2/M3 Ultra 192 GB or 4×A100 80 GB

Should you run this locally?

Yes, for owners of M-series Ultra Macs (the unified memory makes this model uniquely accessible to Mac users) and workstation rigs with 80+ GB VRAM. No, for anyone on consumer GPUs — the model is genuinely workstation-class and partial offload onto consumer DDR5 is too slow to be productive.

How it compares

• vs Llama 4 Scout → Maverick is materially smarter on hard reasoning + dense visual tasks; Scout fits in human-budget hardware. Choose by what you can afford to feed.
• vs Llama 3.3 70B → Maverick wins on quality, multimodality, and long context; Llama 3.3 70B wins on practicality (runs on a single 24 GB card).
• vs Qwen 3 235B-A22B → Qwen 3 235B-A22B is the closest open-weight peer at scale, with similar MoE structure but smaller total params (235B vs 400B). Qwen edges on multilingual; Llama edges on tool use + ecosystem.

Run this yourself

# Mac Studio M2/M3 Ultra example
ollama pull llama4:maverick
ollama run llama4:maverick

Settings: Q4_K_M GGUF, 16384 ctx, MLX or Metal backend, M2 Ultra 192 GB