Llama 4 Maverick 17B 128E Instruct — Hardware Requirements & GPU Compatibility
Vision
Llama 4 Maverick is Meta's instruction-tuned, natively multimodal mixture-of-experts model, with roughly 401.6 billion total parameters and about 17 billion active per token routed across 128 experts. It accepts multilingual text and image input and produces multilingual text and code output, using early fusion to integrate vision and language from pretraining rather than bolting on a separate vision encoder; Meta trained it on roughly 22 trillion tokens of multimodal data with an August 2024 knowledge cutoff. It is built for assistant-style chat and visual reasoning tasks. Despite its relatively small active-parameter count, its total size means it needs a multi-GPU server-class setup to run even once quantized. Context length is up to 1,000,000 tokens. It is released under the Llama 4 Community License, a custom license that is free for most commercial and research use but requires companies with more than 700 million monthly active users to request separate permission from Meta. It was published in April 2025.
10.7K downloads
513 likesSpecifications
- Publisher
- Meta
- Family
- Llama 4
- Parameters
- 401.6B
- Release Date
- 2025-04-01
- License
- Other
Get Started
How Much VRAM Does Llama 4 Maverick 17B 128E Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 187.7 GB | — | 170.67 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 193.3 GB | — | 175.69 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 215.3 GB | — | 195.77 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 220.9 GB | — | 200.79 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 265.1 GB | — | 240.95 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 314.7 GB | — | 286.13 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 364.4 GB | — | 331.31 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 441.7 GB | — | 401.58 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 4 Maverick 17B 128E Instruct?
Q4_K_M · 265.1 GBLlama 4 Maverick 17B 128E Instruct (Q4_K_M) requires 265.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 345+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 4 Maverick 17B 128E Instruct?
Q4_K_M · 265.1 GB3 devices with unified memory can run Llama 4 Maverick 17B 128E Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 512GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Llama 4 Maverick 17B 128E Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Frequently Asked Questions
- How much VRAM does Llama 4 Maverick 17B 128E Instruct need?
Llama 4 Maverick 17B 128E Instruct requires 265.1 GB of VRAM at Q4_K_M, or 883.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 401.6B × 4.8 bits ÷ 8 = 241 GB
KV Cache + Overhead ≈ 24.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M265.1 GB- Can NVIDIA GeForce RTX 5090 run Llama 4 Maverick 17B 128E Instruct?
No — Llama 4 Maverick 17B 128E Instruct requires at least 121.5 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Llama 4 Maverick 17B 128E Instruct?
For Llama 4 Maverick 17B 128E Instruct, Q4_K_M (265.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (303.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 121.5 GB.
VRAM requirement by quantization
IQ2_XXS121.5 GBQ3_K_S193.3 GBQ4_1248.5 GBQ4_K_M ★265.1 GBQ5_K_S303.7 GBBF16883.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 4 Maverick 17B 128E Instruct on a Mac?
Llama 4 Maverick 17B 128E Instruct requires at least 121.5 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 4 Maverick 17B 128E Instruct locally?
Yes — Llama 4 Maverick 17B 128E Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 265.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 4 Maverick 17B 128E Instruct?
At Q4_K_M, Llama 4 Maverick 17B 128E Instruct can reach ~84 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B300 → 8000 ÷ 265.1 × 0.65 = ~208 tok/s
Estimated speed at Q4_K_M (265.1 GB)
~208 tok/s~84 tok/s~84 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 4 Maverick 17B 128E Instruct?
At Q4_K_M, the download is about 240.95 GB. The full-precision BF16 version is 803.17 GB. The smallest option (IQ2_XXS) is 110.44 GB.
- Which GPUs can run Llama 4 Maverick 17B 128E Instruct?
No single consumer GPU has enough VRAM to run Llama 4 Maverick 17B 128E Instruct at Q4_K_M (265.1 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 4 Maverick 17B 128E Instruct?
3 devices with unified memory can run Llama 4 Maverick 17B 128E Instruct at Q4_K_M (265.1 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.