Implement · Meta Llama 4
Can you run it?
Ownership levelPartialnone·limited·partial·substantial·fullAnalytical input C ยท 70.8/100
This page is a projection of the one entry record, the Reliability and Data control factors that Implement covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.
Which domain expands which factor
- AssessUse & modify + Transparency
- ImplementData control + Reliability
- UseReliability
- SupportTransparency
Install & run
Accept the licence on the gated meta-llama organisation, download the safetensors, and serve
with vLLM / transformers or a major cloud endpoint. EU-domiciled entities: confirm you are
licensed before use - the multimodal licence is not granted to you. Pin the exact revision and
verify checksums.
Hardware & VRAM requirements
Mixture-of-experts multimodal models: Scout (109B total / 17B active) fits a high-memory node or
aggressive quantization; Maverick (400B/17B) needs serious multi-GPU infrastructure; Behemoth
(~2T / 288B active) is data-centre-scale. Very long context (Scout advertises up to 10M tokens)
is memory-intensive in practice.
Serving stacks
vLLM, transformers, and every major cloud (Bedrock, Vertex, Together,
Groq) - the broadest toolchain of any open family.
Safe-deployment controls & Deployment Ceiling
Deployment Ceiling: T3 (conditional). First: EU-domiciled entities must not use the Llama
4 multimodal weights (the licence is not granted to you), and all users must check the
700M-MAU commercial trigger and honour the "Built with Llama" naming and the AUP. Then: pair
with Meta's Llama Guard and Prompt Guard, add your own input/output guardrails, add
prompt-injection defences, and treat input images and tool content as untrusted.
Available quantizations
Extensive community GGUF / quantized builds. Use remains bound by the Llama 4 licence
including the EU carve-out - a quant confers no additional rights.
Fine-tuning & adaptation
SFT / LoRA are supported for licensed (non-EU-domiciled) users; derivatives inherit the Llama
conditions and naming. Training data and code are closed, so there is no from-scratch
reproduction.
API / OpenAI-compatible integration
Serve behind an OpenAI-compatible endpoint or a cloud API; handle the Llama-4 chat template,
special tokens, and multimodal (image) inputs at the client.
How this scores
The ownership factors this domain covers, drawn from the one entry record.
3
ReliabilityIs it reliable and good enough for the job?
StrongPerformance 4, operational 5 and safety 4 (with first-party Llama Guard / Prompt Guard): capable multimodal models with the broadest serving and a real guard stack; all at or above 4, so reliability is strong.
How this scores (AOI sub-dimensions)
Operational5/5how practical it is to run, serve and maintain in productionFirst-class ecosystem support: safetensors on every major runtime and cloud endpoint (Bedrock, Vertex, Together, Groq), extensive quantization, and the broadest toolchain of any open family.
Safety4/5whether misuse risks are evaluated and guardrails are providedA genuine strength: the family ships a first-party guard stack - Llama Guard (input/output moderation) and Prompt Guard (prompt-injection/jailbreak detection) - alongside documented safety tuning.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
ModerateSelf-hosting keeps your data local with no telemetry or clawback, but the access gate and the EU-multimodal carve-out mean your right to use the model is conditional on domicile and licence acceptance - moderate.
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
What this means for adoptionFor non-EU users you partially own self-hosted Llama 4: it runs on your infrastructure, performs strongly as a multimodal family, and pairs with Meta's Llama Guard and Prompt Guard (reliability strong), but the Community Licence's 700M-MAU trigger, naming conditions, and access gate keep use-and-modify at moderate - ownership partial. For EU-domiciled individuals and companies the picture is worse: the licence denies you any grant to the multimodal models, so your effective ownership is none - prefer the Llama 3.1 text line (the meta-llama entry, no EU clause) or a non-Llama family. Either way, check the 700M-MAU trigger, honour the Llama attribution terms, and confirm your domicile against the AUP before use.
Sources
The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.
Licenceread2026-08-03
Llama 4 Community License, read verbatim (via Meta's canonical GitHub mirror meta-llama/llama-models): the "Additional Commercial Terms" 700M-MAU threshold ("greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta"), the "Built with Llama" attribution clause, California governing law, and the incorporated AUP carrying the EU multimodal restriction.
Terms of serviceread2026-08-03
Llama 4 Acceptable Use Policy, read verbatim: "With respect to any multimodal models included in Llama 4, the rights granted under Section 1(a) ...
Model cardread2026-08-03
Llama 4 on the verified, access-gated meta-llama HF org: Scout (109B/17B, very long context), Maverick (400B/17B), Behemoth (larger); natively multimodal MoE; safetensors with checksums and a llama4 licence tag; hosted on Bedrock/Vertex/Together/Groq.
Third-party analysisunverified2026-08-03
On public leaderboards Llama 4 Maverick is competitive among open multimodal models; the family adds native multimodality and very long context.
Model cardunverified2026-08-03
Meta ships Llama Guard (input/output moderation) and Prompt Guard (prompt-injection/jailbreak detection) with the Llama family and documents safety tuning; no broad independent multimodal red-team is aggregated here.