Family Llama · 400B parameters

Llama 4 Maverick 400B

400B/17B active MoE (128 experts), natively multimodal. LMArena 1417. 245 GB to download.

🇺🇸 Meta·License Llama 4 Community·Context 976.5625k tokens·Output April 2025← Catalog

01What it can do

Strengths
  • LMArena 1417
  • MMLU-Pro 80
  • 1M context
  • Multimodal
Limitations to know
  • —245 GB download
  • —HF gated
  • —Clause >700M MAU
Architecture
128-expert MoE · 400B/17B active · native multimodal · 1M ctx
Training
Scout’s big brother.
Ideal for
Multimodal frontierAgentsVision

05Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run llama4:maverick
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
240 GB
Q5_K_M
Good quality/size compromise
285 GB
Q8_0
Nearly indistinguishable from FP16
425 GB
FP16
Full precision — server use
800 GB
Fallback CPU · If you don't have a GPU, allow 280 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for Llama 4 Maverick 400B?

To run Llama 4 Maverick 400B locally with Q4 quantization, you need about 240 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — this model exceeds this mini-PC's GPU capacity: choose a smaller model or suitable infrastructure.

Current offer: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395)
AmazonSee price →

Why this choice? Our complete guide on BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) →

Affiliate links — commission possible at no extra cost to you; independent recommendation. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

This model in your private ChatGPT, without the cloud

Too large for your machine? The kit gives you the model that fits in your VRAM

  • Lifetime online access
  • PDF + files
  • Lifetime updates

03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~2t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~8t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~22t/s
RTX 4090, M4 Max, Radeon 7900

04Public benchmarks

Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.

MMLU-Pro
80