Llama 4 Maverick
Meta's high-end Llama 4 sibling — 128 experts MoE built for performance over efficiency. Multilingual strength is its standout. Effectively a server-tier model; consumer hardware can't load it without aggressive quantization and offloading.
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
›Why this rating
8.7/10 — the real Llama 4 flagship for serious local deployment. The 400B-total / 17B-active design wins on quality vs Scout while running at the same speed; the entire question is whether you have the disk and memory.
Overview
Meta's high-end Llama 4 sibling — 128 experts MoE built for performance over efficiency. Multilingual strength is its standout. Effectively a server-tier model; consumer hardware can't load it without aggressive quantization and offloading.
Featured in these stacks
The L3 execution stacks that pick this model as a recommended component, with the one-line note explaining the role it plays in each.
- Stack · L3·Workstation tier·Role: Higher-capability reasoning + vision (when 24GB lets it fit)Build a local vision-model stack (May 2026)
Llama 4 Maverick is the larger variant — better reasoning quality but heavier. AWQ-INT4 makes it borderline-feasible on 24GB; the 5090 32GB is where it comfortably fits with image-token headroom.
- Stack · L3·Production tier·Role: Frontier multimodal MoE4× H100 SXM tensor-parallel workstation — frontier MoE serving reference
Llama 4 Maverick at AWQ-INT4 fits 4× H100 with multimodal headroom. Native vision-text reasoning + 1M context. Pick when multimodal serving is the requirement.
Execution notes
Operator notes
Llama 4 Maverick is Meta's frontier multimodal MoE for May 2026. It's the highest-capability model in the Llama 4 family, designed for native multimodal reasoning rather than vision-as-add-on.
What makes it operator-relevant at the frontier tier:
- Native multimodality — vision tokens interleaved with text from pretraining, not post-hoc adapter-style. Vision-language alignment is sharper than retrofitted VLMs.
- 1M-token context via interleaved attention.
- 17B active params, 400B total — keeps tok/s competitive on the active-param budget.
- Llama Community License — commercial OK with the standard 700M MAU cap.
Deployment notes
This is firmly cluster-only at any practical quant. AWQ-INT4 fits on:
- 4× H100 80GB (320 GB total, comfortable headroom for vision-token KV cache).
- 2× H200 141GB (282 GB) — viable.
- Apple Mac Studio M3 Ultra cluster — multimodal path is throughput-throttled; vision encoder is the bottleneck.
For most operators the access path is hosted inference. The local-deployable Llama 4 in May 2026 is Llama 4 Scout — the L1.25-enriched workstation-cluster sibling. Maverick is the capability ceiling rather than the operator default.
For sub-frontier hardware running Llama-lineage multimodal:
- Workstation tier: Llama 4 Scout is the L1.25-enriched canonical pick.
- Consumer tier: Llama 3.2 11B Vision — same Meta lineage, single-GPU friendly.
Runtime compatibility
- vLLM ✓ excellent. The reference production path; tensor-parallel-size 4 on 4× H100 is the H100 deployment shape.
- SGLang ✓ excellent. Multimodal pipeline + RadixAttention; the prefix-cache wins compound when image inputs are stable across queries.
- Ollama ✗ impractical at this size for multimodal MoE.
- MLX-LM ✓ partial via Exo cluster — research only.
- TensorRT-LLM ✓ best-in-class throughput at scale.
Quantization suitability
AWQ-INT4 is the operational sweet spot. The vision encoder + projection layers are particularly quant-sensitive — Q3-class formats degrade vision-language alignment more than text-only quality drop suggests. Stick to INT4 minimum for vision workloads.
For research-grade benchmarks, FP16 is the reference at ~800 GB total — cluster-only.
Best use cases
- Frontier-tier multimodal agents — UI-grounded agents that reason over screenshots, document Q&A at long context, multi-image reasoning.
- Long-context document analysis with vision — 1M-context + native vision is unique among open-weight in 2026.
- Multimodal RAG at scale — pair with Qwen 3 Embedding 8B and a vision-aware reranker.
When to use a different model
- Single-cluster shipping: Llama 4 Scout is the L1.25-enriched canonical workstation-cluster pick. Maverick is overkill for most production deployments.
- Single-GPU multimodal: Llama 3.2 11B Vision or Pixtral 12B.
- Document-only OCR specialization: InternVL 2.5 78B — sharper on chart and document tasks.
- MIT license requirement: DeepSeek V4 Pro (text-only, but MIT vs Llama Community).
Failure modes specific to this model
- License-cap risk. The Llama Community License's 700M MAU cap is rarely binding but verify before deploying at consumer scale.
- Vision encoder bottleneck. At interactive serving rates, the ViT vision encoder dominates first-token latency. Pre-process image embeddings out of the hot path when possible.
- Cluster cost. 4× H100 minimum is significant. The Scout sibling is the reasonable default; Maverick is for capability-ceiling deployments.
Going deeper
- /stacks/local-vision-model — multimodal deployment recipes
- Llama 4 Scout — L1.25-enriched workstation-cluster sibling
- vLLM operational review — production-recommended runtime
- /maps/inference-runtimes-2026 — runtime ecosystem map
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- 128-expert MoE for top quality
- Strong multilingual coverage
- Best-in-class for Meta family
Weaknesses
- Server-tier only on consumer hardware
- Slower per-token than Scout despite same active params
- Heavy disk footprint
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 240.0 GB | 280 GB |
Get the model
Ollama
One-line install
ollama run llama4:maverickRead our Ollama review →HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Llama 4 Maverick.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Llama 4 Maverick?
Can I use Llama 4 Maverick commercially?
What's the context length of Llama 4 Maverick?
How do I install Llama 4 Maverick with Ollama?
Does Llama 4 Maverick support images?
Source: huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Llama 4 Maverick runs on your specific hardware before committing money.