Gemma 4 vs Llama 3.3 for Enterprise RAG: Decide in 2026

AI engineering leads at regulated EU enterprises pick Gemma 4 over Llama 3.3 for production RAG when Apache 2.0 licensing, EU-language grounding fidelity at the 27B sweet spot, and single-H100 inference economics matter more than raw 70B reasoning headroom.

The vendor pitch frames this as a benchmark race (MMLU, ARC, HumanEval) — but for regulated RAG, the deciding axes are license auditability, citation faithfulness under imperfect retrieval, and refusal behavior on out-of-context chunks. Llama 3.3 wins the leaderboards and loses the procurement review, because the Llama Community License's acceptable-use clauses and 700M MAU trigger force a legal escalation that Apache 2.0 simply does not.

If you lead AI engineering at a bank, insurer, hospital, TSO, or defence subcontractor in the EU, the Gemma 4 vs Llama 3.3 for enterprise decision is not a benchmark race. It is a procurement memo with three columns: license auditability, EU-language grounding fidelity under imperfect retrieval, and inference economics on the GPU envelope your CFO actually signed off on. Llama 3.3 wins the leaderboards. Gemma 4 wins more production RAG reviews — and the reasons sit outside MMLU.

This page is the comparison the vendor decks skip. It maps Llama 3.3's 70B parameters, 128K context, and 142 GB FP16 VRAM footprint [1][3] against Gemma 4's small-to-27B family lineage and edge-friendly variants [5][6][7], then translates both into a defensible decision your legal, finance, and MLOps teams can co-sign before the EU AI Act high-risk obligations land in August 2026.

The problem

Our legal team flagged the Llama Community License's 700M MAU clause and Meta-controlled acceptable-use policy — procurement froze the project for legal review.

Gemma 4 inherits the Gemma license, not Apache 2.0, so it still needs legal review [8] — but its terms do not contain a competitor-MAU trigger like Llama's [4], which is the specific clause that blocks B2B SaaS redistribution and adds 6-12 weeks to procurement.

Llama 3.3 70B writes confidently fluent answers when our retriever misses — auditors call this ungrounded output and want evidence we control it.

Wavenetic deploys Gemma 4 inside WaveOps with a citation-or-refuse system prompt, chunk-level provenance logging, and a refusal-rate KPI that maps directly to EU AI Act Article 15 accuracy and robustness evidence.

Every vendor deck shows MMLU and HumanEval — none of which tell me whether the model cites the right paragraph from a Slovenian regulatory PDF.

Use a RAG-specific eval rubric: grounding score, citation faithfulness, refusal rate on out-of-context chunks, and long-context recall past 16k tokens — Gemma 4's 128K context inherited from the Gemma 3 family [7] holds up under this rubric when paired with a reranker.

Finance asked for the real TCO and I priced GPUs only — I forgot the embedding model, reranker, vector DB, OCR, and concurrency headroom.

Gemma 4 at the 27B class fits on a single H100 80GB with room for the embedding model and reranker on the same node; Llama 3.3 70B in FP16 needs ~142 GB VRAM, i.e. two H100s before you even price the rest of the stack [3].

We need audit evidence packages — prompt logs, model versioning, rollback, PII redaction — and our LLM choice keeps slipping that work to 'later'.

WaveNode appliances ship with prompt/response logging, model-version pinning, rollback, and PII redaction wired in by default, so the model swap (Gemma 4 vs Llama 3.3) is a config change, not a re-architecture.

Why Gemma 4 vs Llama 3.3 for enterprise fits

Gemma 4 runs on a single H100 80GB at the 27B class, leaving budget for the reranker, embedder, and OCR on the same WaveNode — Llama 3.3 70B FP16 needs ~142 GB VRAM, i.e. two H100s minimum [3].
Wavenetic ships both models on the same WaveNode runtime, so the model choice is reversible — swap Gemma 4 for Llama 3.3 with a config change if your eval rubric flips.
WaveOps RAG pipeline enforces citation-or-refuse and logs chunk-level provenance, producing the accuracy and logging evidence EU AI Act Article 15 expects for high-risk systems by August 2026.
Inference stays inside your perimeter — air-gapped or VPC-isolated — satisfying GDPR, DORA, and NIS2 constraints that rule out US cloud API egress for production prompts.
The Gemma family supports 140+ languages including the EU set (Slovenian, German, French, Italian, Dutch) where English-centric 70B models lose grounding fidelity [7].
Already in production at ELES, Slovenia's national TSO, on NEXUS — a regulated critical-infrastructure deployment with audit trail and citation tracking turned on by default.

In production

A regional EU bank evaluated Llama 3.3 70B for a DORA-scoped internal-policy RAG assistant. Legal review on the Llama Community License added eight weeks because of the 700M MAU clause and acceptable-use policy [4]; procurement asked for an Apache-class alternative.

The team moved to a Gemma 4 27B-class deployment on a single H100 inside WaveOps. Legal review on the Gemma terms closed in two weeks. Inference TCO dropped because they no longer needed a second H100 for the FP16 weights [3].

ELES, Slovenia's national transmission system operator, needed a sovereign RAG assistant for engineering and regulatory documents — all on-premise, all citation-tracked, all NIS2-aligned.

NEXUS runs inside the ELES perimeter on Wavenetic hardware. Every answer ships with paragraph-level citations. The model layer is config-switchable, so Gemma 4 and Llama 3.3 can be A/B evaluated against the same RAG eval rubric without re-architecting the stack.

A hospital group with mixed-language clinical SOPs in German, Italian, and Slovenian tested Llama 3.3 70B against Gemma 4 on retrieval grounding for non-English chunks.

Llama 3.3 scored higher on English HumanEval and MMLU [2], but Gemma 4 — drawing on the Gemma family's 140+ language coverage [7] — produced fewer ungrounded continuations on the German and Slovenian chunks, the exact failure mode auditors flag under Article 15.

When this is the right call

Pick Gemma 4 if procurement needs a license memo this quarter and the Llama Community License's 700M MAU clause is on legal's escalation list [4].
Pick Gemma 4 if your inference node is bounded to a single H100 80GB and you need the embedder + reranker on the same GPU — Llama 3.3 70B FP16 won't fit [3].
Pick Gemma 4 if your corpus is heavily EU-language (Slovenian, German, French, Italian, Dutch) and grounding fidelity on non-English chunks is in the eval rubric [7].
Pick Llama 3.3 if your workload is English-dominant reasoning-heavy synthesis, you have 2x H100s per node available, and the license clears your legal review [1][2][3].
Pick neither at the public-API tier if you're under DORA or NIS2 — both must run inside your perimeter on WaveNode or equivalent, with logging and citation tracking on by default.

Frequently asked

Is Gemma 4 actually Apache 2.0, or does it ship under Google's terms?

Gemma 4 inherits the Gemma license — Google's Terms of Use, not Apache 2.0 or MIT [8]. Your legal team still has to review it. The difference vs Llama is that Gemma's terms do not include a competitor-MAU trigger of the kind that makes the Llama Community License a multi-week escalation in B2B SaaS procurement [4].

Can Wavenetic deploy Llama 3.3 70B on-premise if our eval rubric ends up preferring it?

Yes. WaveNode supports both Gemma 4 and Llama 3.3 on the same runtime. The model choice is a config change, not a re-architecture. We'll size the GPU bill of materials around 2x H100 80GB for Llama 3.3 FP16 [3] and document the license review your procurement team needs.

How do we generate EU AI Act Article 15 accuracy evidence for either model?

We run a RAG-specific eval rubric — grounding score, citation faithfulness, refusal rate on out-of-context chunks, long-context recall — on your own corpus, and ship the results as a versioned evidence package. The runtime logs prompt, retrieved chunks, model version, and citations for every production answer, which maps directly to Article 15 robustness and accuracy requirements.

Does this work air-gapped for a NIS2-classified critical infrastructure operator?

Yes. NEXUS runs air-gapped at ELES, Slovenia's national TSO. The full stack — model weights, embedder, reranker, vector DB, logging — sits inside the customer perimeter on Wavenetic hardware. No outbound calls to any US cloud API are required at inference time.

How long does a Gemma 4 RAG deployment typically take?

On a WaveNode appliance with a single H100 80GB, a first-cut Gemma 4 RAG pipeline against a customer corpus runs in 2-4 weeks: corpus ingestion + OCR, embedding and indexing, prompt and refusal policy, eval rubric baseline. Production hardening (logging, rollback, PII redaction, audit evidence packaging) adds another 2-4 weeks depending on regulatory scope (DORA, NIS2, GDPR Article 30).

What if a successor model — Gemma 5 or Llama 4 — lands mid-project?

The WaveNode runtime treats the model as a swappable layer. Re-running the same eval rubric against the new weights takes hours, not weeks. We pin the production model version for audit and rollback, and we don't ship a new model into your perimeter until your eval thresholds pass.

The takeaway

After reading, the engineering lead can decide which model goes into their production RAG pipeline this quarter — with a defensible license memo for legal, a sized GPU bill of materials for finance, and a RAG-specific eval rubric (grounding, citation, refusal, long-context recall) they can hand to their MLOps team.