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.
Our verdict
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.
- No configuration-matched speed measurement is linked for this option.
- 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.
Performance evidence
The earlier timing ranges were not tied to a published run. Use the benchmark records for measured results and compare their full configurations.
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 |
At Q4 Llama 4 Maverick needs about 280 GB of memory by our estimate, more than a 32 GB graphics card holds. Renting a GPU with enough memory (or several GPUs) by the hour is the practical way to try it before you plan hardware for it.
Referral links: RunLocalAI may earn a commission or account credits if you sign up and rent, at no extra cost to you. Hourly rates and which GPUs are listed change all the time, so check the provider's page before you start. How we make money.
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.