Gemma 4 vs Llama 3

Compare Gemma 4 and Llama 3 — Google's and Meta's flagship open-weight families. Architecture, native multimodal capability, edge deployment, licensing, and fine-tuning trade-offs.

Overview

Gemma 4 and Llama 3 are the two flagship open-weight families from Google and Meta, and they take meaningfully different approaches to model design. Gemma 4 spans a wide range of sizes — from the 2B effective edge model (e2b) up to the 31B dense flagship — with native multimodal capability across the entire family. Llama 3 spans 8B to 405B in dense-only configurations and is text-only at the base level (multimodal extensions exist but are not part of the core release).

The headline change with Gemma 4's April 2026 release is licensing. Gemma 4 is the first generation of Gemma released under Apache 2.0, replacing the custom Gemma License that constrained Gemma 1-3 commercial deployments. This brings Gemma 4 into licensing parity with Qwen, Mistral, and OLMo, and removes a major friction point for commercial integration. Llama 3 retains its Llama Community License with usage-cap and attribution requirements.

Feature Comparison

Feature	Gemma 4	Llama 3
Parameter Sizes	e2b (~2B), e4b (~4B), 26B-A3.8B, 31B	8B, 70B, 405B
Smallest Variant	e2b (~2B effective, mobile-deployable)	8B (laptop-class)
Architecture	Dense + MoE	Dense only
Context Window	128K tokens	128K tokens
License	Apache 2.0 (new in Gemma 4)	Llama Community License
Native Multimodal	Yes — across all sizes	No (text-only base)
Multilingual Coverage	140+ languages	~30 languages, English-dominant
On-Device Deployment	Native (e2b ≈ 1.5GB at Q4_K_M)	8B at Q4_K_M ≈ 4.5GB
Built-in Safety Stack	ShieldGemma classifier, content-safety post-training	Llama Guard 3 (separate model)
MLX / Apple Silicon Support	First-class	Mature

Strengths

Gemma 4

Apache 2.0 licensing — first Gemma generation with this permissive license, eliminating prior commercial deployment friction
Native multimodal across the entire family — even the 2B effective e2b accepts image input, unprecedented for that size
Smallest variants (e2b, e4b) enable on-device deployment patterns that Llama 3's 8B minimum can't reach
140+ language training coverage is broader than Llama 3, particularly for European and Asian languages
Built-in safety stack (ShieldGemma) is integrated rather than requiring a separate Llama Guard 3 deployment

Llama 3

Substantially larger and more mature ecosystem of fine-tunes, deployment recipes, and community resources
405B variant has no Gemma 4 equivalent — Llama 3 405B remains a strong choice for high-quality teacher models
Broader third-party adoption — most AI products integrate Llama 3 first, with Gemma support coming later if at all
More predictable behavior in tool-use and function-calling scenarios with longer track record in production
Quantization recipes and Q4/Q5/Q6 variants have years of community optimization behind them

Which Should You Choose?

You're deploying AI on phones, embedded devices, or other small-memory targetsGemma 4

Gemma 4 e2b at Q4_K_M is approximately 1.5GB and runs on phones or any device with 4GB+ memory. Llama 3's smallest 8B variant needs ~5GB and is impractical on most phones. The native multimodal support also unlocks camera-based on-device applications.

You need a 70B-class or larger model for high-quality serving or as a teacher modelLlama 3

Gemma 4 tops out at 31B dense / 26B-A3.8B MoE. Llama 3 70B and 405B remain the open-weight choices when you specifically need the capability that comes from larger parameter counts.

Your commercial deployment is sensitive to license restrictions or attribution requirementsGemma 4

Gemma 4's new Apache 2.0 license is the cleanest commercial option. Llama 3's Community License includes usage caps (700M monthly active users) and attribution requirements that complicate certain commercial use cases.

You're drawing on existing fine-tunes, training data, or community resourcesLlama 3

Llama 3 has a substantially larger ecosystem of pre-built fine-tunes, training data formats, and community-validated recipes. For teams who benefit from this maturity, Llama 3 has a meaningful head start.

Verdict

Gemma 4 is the better choice for on-device, edge, and consumer deployment patterns where its small variants and native multimodal capability create capabilities Llama 3 simply can't match. Llama 3 is the better choice when you need 70B+ scale, want to draw on the broadest open-weight ecosystem, or already have Llama-based pipelines in production. The two families are complementary rather than directly substitutable.

For 2026 commercial deployments starting fresh, Gemma 4's Apache 2.0 licensing is a meaningful structural advantage — it eliminates a category of legal review that Llama 3 still requires. For deployments inheriting Llama-based infrastructure, the migration cost usually outweighs the licensing benefit. Many teams now run Gemma 4 for edge and consumer-facing features alongside Llama 3 for server-side high-quality serving.

How Ertas Fits In

Both Gemma 4 and Llama 3 are well-supported in Ertas Studio's fine-tuning pipeline. Gemma 4's MoE 26B-A3.8B variant offers particularly efficient fine-tuning given its 3.8B active parameter count — QLoRA fits comfortably on a 24GB GPU at full sequence lengths. The Gemma 4 e2b and e4b variants also fine-tune on consumer GPUs (6-12GB VRAM), making them practical starting points for on-device specialization.

For multimodal fine-tuning, Gemma 4 is the natural choice — its base architecture supports image input across all variants, and Ertas Studio supports interleaved text-and-image training data formats. Llama 3 multimodal fine-tuning requires using a multimodal extension (Llama 3.2 Vision or third-party VLM derivative), which adds complexity. After training, Ertas Studio exports both Gemma 4 and Llama 3 fine-tunes to GGUF for deployment via Ollama, llama.cpp, or LM Studio with single-click compatibility.